To the Editor:

A 40-year-old white woman presented with a waxing and waning erythematous pruritic rash on the chest, back, and axillae of 3 years’ duration. The appearance of the rash coincided with an intentional weight loss of more than 100 lb, achieved through various diets, most recently a Paleolithic (paleo) diet that was high in protein; low in carbohydrates; and specifically restricted dairy, cereal grains, refined sugars, processed foods, white potatoes, salt, refined oils, and legumes.¹ The patient had been monitoring blood glucose and ketone levels. Prior to presentation, she received various treatments including clotrimazole cream and topical steroids with no improvement. 

On physical examination, there were scaly, pink-red, reticulated papules and plaques coexisting with tan reticulated patches that were symmetrically distributed on the central back, lateral and central chest (Figure 1A), breasts, and inframammary areas. During the most severe flare-up, the blood ketones measured 1 mmol/L. There was no relevant medical history. She was of Spanish and Italian descent.

Prurigo pigmentosa is a rare inflammatory dermatosis that was initially described in 1971 as “a peculiar pruriginous dermatosis with gross reticular pigmentation” by Nagashima et al.² Prurigo pigmentosa is most frequently diagnosed in Japan, and since its discovery, it has been reported in more than 300 cases worldwide.^2-4

Fewer than 50 non-Japanese cases have been reported, with the possibility of an additional ethnic predisposition among the Turkish and Sicilian populations, though only 6 cases have been reported in the United States.^3-6 Prurigo pigmentosa tends to occur in the spring and summer months and is most common among young females, with a mean age of 24 years. The typical lesions of PP are symmetrically distributed on the trunk with a tendency to localize on the upper back, nape of the neck, and intermammary and inframammary regions. Eruptions have been reported to occur on additional areas; however, mucus membranes are always spared.⁶

Individual lesions differ in appearance depending on the stage of presentation and are categorized as early, fully developed, resolving, and late lesions.⁶ Pruritic macules and papules are present early in the disease state and resolve into crusted and/or scaly papules followed by pigmented macules. Early lesions tend to be intensely pruritic with signs of excoriation, while resolving lesions lack symptoms. Lesions last approximately 1 week but tend to reappear at the site where they were previously present, which allows for lesions of different ages to coexist, appearing in a reticular arrangement with hyperpigmented mottling lasting from a few weeks to months.⁶

Just as the clinical picture transpires rapidly within 1 week, so do the histopathologic findings.⁶ Early lesions are categorized by a superficial perivascular and interstitial infiltrate of neutrophils, spongiosis, ballooning, and necrotic keratinocytes. These early lesions are present for less than 48 hours, and these histopathologic findings are diagnostic of PP. Within 2 days, lymphocytes predominate in the dermal infiltrate, and a patchy lichenoid aspect is established in the fully developed lesion along with reticular and vacuolar alterations. Late lesions show a parakeratotic and hyperpigmented epidermis with melanophages present in the papillary and reticular dermis. At this last stage, the histopathologic features of PP are indistinguishable from any other disease that results in postinflammatory hyperpigmentation, making diagnosis difficult.⁶

A variety of therapeutic options are used in the treatment of PP, with the most effective agents being oral antibiotics including dapsone, minocycline, and doxycycline, all of which limit the local tissue inflammatory response and cytotoxic effects. Topical and systemic antihistamines as well as corticosteroids are ineffective and have not been shown to prevent the postinflammatory reticular pigmentation.^6-10

Various underlying factors have been associated with PP, including friction, heat, sunlight, sweating, allergic contact sensitization, and ketosis due to nutritional deficiency or diabetes mellitus; however; the exact etiology remains ambiguous.^2-7 The association with ketosis and nutrition is of particular interest in this case. Onset of PP has been reported to coincide with dieting, fasting, weight loss, anorexia nervosa, and diabetes mellitus.^3,6-9 Roughly 50 patients with PP had ketosis subsequent to these metabolic disturbances.^3,6-10 As of now, the only reported correlation between ketosis and PP is that upon diet modification, lesions resolved following ketone normalization, as was observed in our patient.^3,6-8 Reports of PP in diabetic patients while in ketoacidosis describe resolution of lesions with insulin administration.^6-9 The pathophysiology of ketosis and its association with PP is unclear; however, the similarities seen in the immune response of PP and that stimulated by ketosis may expose an associated mechanism.

Ketosis is a temporary condition characterized by elevated serum ketones that are used as an alternative energy source when blood glucose is low or insulin is deficient.¹¹ The most common causes of ketosis are the physiologic responses to fasting, prolonged exercise, or a high-protein/low-carbohydrate diet, though pathologic causes include insulin-dependent diabetes mellitus, alcoholism, and salicylate overdose.¹¹ In healthy individuals, blood ketone levels rarely approach 0.5 mmol/L. Prolonged fasting or restricting intake of carbohydrates to less than 40 g daily can induce mild ketosis that resolves with re-introduction of carbohydrates.¹¹

Ketone bodies pass from the circulating blood into tissues or remain near the blood vessels, inducing cytotoxic effects and perivascular inflammation.^10,11 Increased ketone bodies have been shown to upregulate intercellular adhesion molecule 1 (ICAM-1) and leukocyte function-associated antigen 1 (LFA-1), a phenomenon also seen in lesional keratinocytes of PP.^12,13 Teraki et al¹³ observed that epidermal keratinocytes exhibited increased expression of ICAM-1 as well as intense expression of LFA-1 on dermal and epidermotropic leukocytes, which was thought to be due to cell-mediated cytotoxicity. Not only do increased ketone bodies upregulate ICAM-1 and LFA-1, but they also are involved in increasing many proinflammatory mediators that may be capable of inducing the response seen in PP keratinocytes.^12,13

Intercellular adhesion molecule 1 is important in initiating cellular interactions in the immune response and is the ligand for LFA-1 found on most leukocytes.¹⁴ Increased ICAM-1/LFA-1 interaction is thought to be the major pathway by which leukocytes are able to attach to keratinocytes and endothelial cells, allowing for leukocyte tissue migration and specific immunologic reactions, including leukocyte-mediated cytotoxicity. Interestingly, glucocorticoids are ineffective in reducing the expression of ICAM-1 in cultured keratinocytes.¹⁴ This connection between ketosis and inflammation that results in leukocyte migration and ultimately keratinocyte cytotoxicity may well be fundamental to the pathophysiology of PP and may provide a possible explanation for the ineffectiveness of corticosteroid treatment.

Middleton and Norris¹⁵ observed that individual keratinocyte strains show considerable variability in ICAM-1 expression that was found to be attributable to genetic polymorphisms. The presence of a particular polymorphism affecting ICAM-1 expression on human keratinocytes may explain the apparent ethnogeographic predisposition of PP as well as the ease at which ICAM-1 is expressed in the presence of ketones.

We describe a case of a 40-year-old white woman who was diagnosed with PP that was prompted by a 100-lb weight loss and self-induced ketosis while following a paleo diet with carbohydrate restriction. Successful treatment was attained through diet modification alone. This interesting case was another instance in which the pathophysiology of PP was attributed to ketosis. Because not all patients that are in ketosis have PP, larger prospective cohort studies are needed to further elucidate the association of PP and ketosis.

To the Editor:

A 40-year-old white woman presented with a waxing and waning erythematous pruritic rash on the chest, back, and axillae of 3 years’ duration. The appearance of the rash coincided with an intentional weight loss of more than 100 lb, achieved through various diets, most recently a Paleolithic (paleo) diet that was high in protein; low in carbohydrates; and specifically restricted dairy, cereal grains, refined sugars, processed foods, white potatoes, salt, refined oils, and legumes.¹ The patient had been monitoring blood glucose and ketone levels. Prior to presentation, she received various treatments including clotrimazole cream and topical steroids with no improvement. 

On physical examination, there were scaly, pink-red, reticulated papules and plaques coexisting with tan reticulated patches that were symmetrically distributed on the central back, lateral and central chest (Figure 1A), breasts, and inframammary areas. During the most severe flare-up, the blood ketones measured 1 mmol/L. There was no relevant medical history. She was of Spanish and Italian descent.

Prurigo pigmentosa is a rare inflammatory dermatosis that was initially described in 1971 as “a peculiar pruriginous dermatosis with gross reticular pigmentation” by Nagashima et al.² Prurigo pigmentosa is most frequently diagnosed in Japan, and since its discovery, it has been reported in more than 300 cases worldwide.^2-4

Fewer than 50 non-Japanese cases have been reported, with the possibility of an additional ethnic predisposition among the Turkish and Sicilian populations, though only 6 cases have been reported in the United States.^3-6 Prurigo pigmentosa tends to occur in the spring and summer months and is most common among young females, with a mean age of 24 years. The typical lesions of PP are symmetrically distributed on the trunk with a tendency to localize on the upper back, nape of the neck, and intermammary and inframammary regions. Eruptions have been reported to occur on additional areas; however, mucus membranes are always spared.⁶

Individual lesions differ in appearance depending on the stage of presentation and are categorized as early, fully developed, resolving, and late lesions.⁶ Pruritic macules and papules are present early in the disease state and resolve into crusted and/or scaly papules followed by pigmented macules. Early lesions tend to be intensely pruritic with signs of excoriation, while resolving lesions lack symptoms. Lesions last approximately 1 week but tend to reappear at the site where they were previously present, which allows for lesions of different ages to coexist, appearing in a reticular arrangement with hyperpigmented mottling lasting from a few weeks to months.⁶

Just as the clinical picture transpires rapidly within 1 week, so do the histopathologic findings.⁶ Early lesions are categorized by a superficial perivascular and interstitial infiltrate of neutrophils, spongiosis, ballooning, and necrotic keratinocytes. These early lesions are present for less than 48 hours, and these histopathologic findings are diagnostic of PP. Within 2 days, lymphocytes predominate in the dermal infiltrate, and a patchy lichenoid aspect is established in the fully developed lesion along with reticular and vacuolar alterations. Late lesions show a parakeratotic and hyperpigmented epidermis with melanophages present in the papillary and reticular dermis. At this last stage, the histopathologic features of PP are indistinguishable from any other disease that results in postinflammatory hyperpigmentation, making diagnosis difficult.⁶

A variety of therapeutic options are used in the treatment of PP, with the most effective agents being oral antibiotics including dapsone, minocycline, and doxycycline, all of which limit the local tissue inflammatory response and cytotoxic effects. Topical and systemic antihistamines as well as corticosteroids are ineffective and have not been shown to prevent the postinflammatory reticular pigmentation.^6-10

Various underlying factors have been associated with PP, including friction, heat, sunlight, sweating, allergic contact sensitization, and ketosis due to nutritional deficiency or diabetes mellitus; however; the exact etiology remains ambiguous.^2-7 The association with ketosis and nutrition is of particular interest in this case. Onset of PP has been reported to coincide with dieting, fasting, weight loss, anorexia nervosa, and diabetes mellitus.^3,6-9 Roughly 50 patients with PP had ketosis subsequent to these metabolic disturbances.^3,6-10 As of now, the only reported correlation between ketosis and PP is that upon diet modification, lesions resolved following ketone normalization, as was observed in our patient.^3,6-8 Reports of PP in diabetic patients while in ketoacidosis describe resolution of lesions with insulin administration.^6-9 The pathophysiology of ketosis and its association with PP is unclear; however, the similarities seen in the immune response of PP and that stimulated by ketosis may expose an associated mechanism.

Ketosis is a temporary condition characterized by elevated serum ketones that are used as an alternative energy source when blood glucose is low or insulin is deficient.¹¹ The most common causes of ketosis are the physiologic responses to fasting, prolonged exercise, or a high-protein/low-carbohydrate diet, though pathologic causes include insulin-dependent diabetes mellitus, alcoholism, and salicylate overdose.¹¹ In healthy individuals, blood ketone levels rarely approach 0.5 mmol/L. Prolonged fasting or restricting intake of carbohydrates to less than 40 g daily can induce mild ketosis that resolves with re-introduction of carbohydrates.¹¹

Ketone bodies pass from the circulating blood into tissues or remain near the blood vessels, inducing cytotoxic effects and perivascular inflammation.^10,11 Increased ketone bodies have been shown to upregulate intercellular adhesion molecule 1 (ICAM-1) and leukocyte function-associated antigen 1 (LFA-1), a phenomenon also seen in lesional keratinocytes of PP.^12,13 Teraki et al¹³ observed that epidermal keratinocytes exhibited increased expression of ICAM-1 as well as intense expression of LFA-1 on dermal and epidermotropic leukocytes, which was thought to be due to cell-mediated cytotoxicity. Not only do increased ketone bodies upregulate ICAM-1 and LFA-1, but they also are involved in increasing many proinflammatory mediators that may be capable of inducing the response seen in PP keratinocytes.^12,13

Intercellular adhesion molecule 1 is important in initiating cellular interactions in the immune response and is the ligand for LFA-1 found on most leukocytes.¹⁴ Increased ICAM-1/LFA-1 interaction is thought to be the major pathway by which leukocytes are able to attach to keratinocytes and endothelial cells, allowing for leukocyte tissue migration and specific immunologic reactions, including leukocyte-mediated cytotoxicity. Interestingly, glucocorticoids are ineffective in reducing the expression of ICAM-1 in cultured keratinocytes.¹⁴ This connection between ketosis and inflammation that results in leukocyte migration and ultimately keratinocyte cytotoxicity may well be fundamental to the pathophysiology of PP and may provide a possible explanation for the ineffectiveness of corticosteroid treatment.

Middleton and Norris¹⁵ observed that individual keratinocyte strains show considerable variability in ICAM-1 expression that was found to be attributable to genetic polymorphisms. The presence of a particular polymorphism affecting ICAM-1 expression on human keratinocytes may explain the apparent ethnogeographic predisposition of PP as well as the ease at which ICAM-1 is expressed in the presence of ketones.

We describe a case of a 40-year-old white woman who was diagnosed with PP that was prompted by a 100-lb weight loss and self-induced ketosis while following a paleo diet with carbohydrate restriction. Successful treatment was attained through diet modification alone. This interesting case was another instance in which the pathophysiology of PP was attributed to ketosis. Because not all patients that are in ketosis have PP, larger prospective cohort studies are needed to further elucidate the association of PP and ketosis.

To the Editor:

A 40-year-old white woman presented with a waxing and waning erythematous pruritic rash on the chest, back, and axillae of 3 years’ duration. The appearance of the rash coincided with an intentional weight loss of more than 100 lb, achieved through various diets, most recently a Paleolithic (paleo) diet that was high in protein; low in carbohydrates; and specifically restricted dairy, cereal grains, refined sugars, processed foods, white potatoes, salt, refined oils, and legumes.¹ The patient had been monitoring blood glucose and ketone levels. Prior to presentation, she received various treatments including clotrimazole cream and topical steroids with no improvement. 

On physical examination, there were scaly, pink-red, reticulated papules and plaques coexisting with tan reticulated patches that were symmetrically distributed on the central back, lateral and central chest (Figure 1A), breasts, and inframammary areas. During the most severe flare-up, the blood ketones measured 1 mmol/L. There was no relevant medical history. She was of Spanish and Italian descent.

Prurigo pigmentosa is a rare inflammatory dermatosis that was initially described in 1971 as “a peculiar pruriginous dermatosis with gross reticular pigmentation” by Nagashima et al.² Prurigo pigmentosa is most frequently diagnosed in Japan, and since its discovery, it has been reported in more than 300 cases worldwide.^2-4

Fewer than 50 non-Japanese cases have been reported, with the possibility of an additional ethnic predisposition among the Turkish and Sicilian populations, though only 6 cases have been reported in the United States.^3-6 Prurigo pigmentosa tends to occur in the spring and summer months and is most common among young females, with a mean age of 24 years. The typical lesions of PP are symmetrically distributed on the trunk with a tendency to localize on the upper back, nape of the neck, and intermammary and inframammary regions. Eruptions have been reported to occur on additional areas; however, mucus membranes are always spared.⁶

Individual lesions differ in appearance depending on the stage of presentation and are categorized as early, fully developed, resolving, and late lesions.⁶ Pruritic macules and papules are present early in the disease state and resolve into crusted and/or scaly papules followed by pigmented macules. Early lesions tend to be intensely pruritic with signs of excoriation, while resolving lesions lack symptoms. Lesions last approximately 1 week but tend to reappear at the site where they were previously present, which allows for lesions of different ages to coexist, appearing in a reticular arrangement with hyperpigmented mottling lasting from a few weeks to months.⁶

Just as the clinical picture transpires rapidly within 1 week, so do the histopathologic findings.⁶ Early lesions are categorized by a superficial perivascular and interstitial infiltrate of neutrophils, spongiosis, ballooning, and necrotic keratinocytes. These early lesions are present for less than 48 hours, and these histopathologic findings are diagnostic of PP. Within 2 days, lymphocytes predominate in the dermal infiltrate, and a patchy lichenoid aspect is established in the fully developed lesion along with reticular and vacuolar alterations. Late lesions show a parakeratotic and hyperpigmented epidermis with melanophages present in the papillary and reticular dermis. At this last stage, the histopathologic features of PP are indistinguishable from any other disease that results in postinflammatory hyperpigmentation, making diagnosis difficult.⁶

A variety of therapeutic options are used in the treatment of PP, with the most effective agents being oral antibiotics including dapsone, minocycline, and doxycycline, all of which limit the local tissue inflammatory response and cytotoxic effects. Topical and systemic antihistamines as well as corticosteroids are ineffective and have not been shown to prevent the postinflammatory reticular pigmentation.^6-10

Various underlying factors have been associated with PP, including friction, heat, sunlight, sweating, allergic contact sensitization, and ketosis due to nutritional deficiency or diabetes mellitus; however; the exact etiology remains ambiguous.^2-7 The association with ketosis and nutrition is of particular interest in this case. Onset of PP has been reported to coincide with dieting, fasting, weight loss, anorexia nervosa, and diabetes mellitus.^3,6-9 Roughly 50 patients with PP had ketosis subsequent to these metabolic disturbances.^3,6-10 As of now, the only reported correlation between ketosis and PP is that upon diet modification, lesions resolved following ketone normalization, as was observed in our patient.^3,6-8 Reports of PP in diabetic patients while in ketoacidosis describe resolution of lesions with insulin administration.^6-9 The pathophysiology of ketosis and its association with PP is unclear; however, the similarities seen in the immune response of PP and that stimulated by ketosis may expose an associated mechanism.

Ketosis is a temporary condition characterized by elevated serum ketones that are used as an alternative energy source when blood glucose is low or insulin is deficient.¹¹ The most common causes of ketosis are the physiologic responses to fasting, prolonged exercise, or a high-protein/low-carbohydrate diet, though pathologic causes include insulin-dependent diabetes mellitus, alcoholism, and salicylate overdose.¹¹ In healthy individuals, blood ketone levels rarely approach 0.5 mmol/L. Prolonged fasting or restricting intake of carbohydrates to less than 40 g daily can induce mild ketosis that resolves with re-introduction of carbohydrates.¹¹

Ketone bodies pass from the circulating blood into tissues or remain near the blood vessels, inducing cytotoxic effects and perivascular inflammation.^10,11 Increased ketone bodies have been shown to upregulate intercellular adhesion molecule 1 (ICAM-1) and leukocyte function-associated antigen 1 (LFA-1), a phenomenon also seen in lesional keratinocytes of PP.^12,13 Teraki et al¹³ observed that epidermal keratinocytes exhibited increased expression of ICAM-1 as well as intense expression of LFA-1 on dermal and epidermotropic leukocytes, which was thought to be due to cell-mediated cytotoxicity. Not only do increased ketone bodies upregulate ICAM-1 and LFA-1, but they also are involved in increasing many proinflammatory mediators that may be capable of inducing the response seen in PP keratinocytes.^12,13

Intercellular adhesion molecule 1 is important in initiating cellular interactions in the immune response and is the ligand for LFA-1 found on most leukocytes.¹⁴ Increased ICAM-1/LFA-1 interaction is thought to be the major pathway by which leukocytes are able to attach to keratinocytes and endothelial cells, allowing for leukocyte tissue migration and specific immunologic reactions, including leukocyte-mediated cytotoxicity. Interestingly, glucocorticoids are ineffective in reducing the expression of ICAM-1 in cultured keratinocytes.¹⁴ This connection between ketosis and inflammation that results in leukocyte migration and ultimately keratinocyte cytotoxicity may well be fundamental to the pathophysiology of PP and may provide a possible explanation for the ineffectiveness of corticosteroid treatment.

Middleton and Norris¹⁵ observed that individual keratinocyte strains show considerable variability in ICAM-1 expression that was found to be attributable to genetic polymorphisms. The presence of a particular polymorphism affecting ICAM-1 expression on human keratinocytes may explain the apparent ethnogeographic predisposition of PP as well as the ease at which ICAM-1 is expressed in the presence of ketones.

We describe a case of a 40-year-old white woman who was diagnosed with PP that was prompted by a 100-lb weight loss and self-induced ketosis while following a paleo diet with carbohydrate restriction. Successful treatment was attained through diet modification alone. This interesting case was another instance in which the pathophysiology of PP was attributed to ketosis. Because not all patients that are in ketosis have PP, larger prospective cohort studies are needed to further elucidate the association of PP and ketosis.

Ichthyosis vulgaris
 

Ichthyoses describe a group of disorders of cornification in which the epidermis differentiates abnormally, leading to generalized scaling of the skin. Ichthyosis is derived from the Greek word for fish, “ichthys.” Ichthyosis vulgaris is the most common of these conditions and often presents in early childhood during the first year of life. It is inherited in an autosomal-dominant pattern. Skin is dry and scaly over the entire body, although the antecubital and popliteal fossa may be uninvolved. The scalp may be involved as well. Atopy and keratosis pilaris may be associated. By adulthood, symptoms tend to abate.

Dr. Bilu Martin is a board-certified dermatologist in private practice at Premier Dermatology, MD, in Aventura, Fla. More diagnostic cases are available at mdedge.com/edermatologynews.com. To submit a case for possible publication, send an email to [email protected].

Ichthyosis vulgaris
 

Ichthyoses describe a group of disorders of cornification in which the epidermis differentiates abnormally, leading to generalized scaling of the skin. Ichthyosis is derived from the Greek word for fish, “ichthys.” Ichthyosis vulgaris is the most common of these conditions and often presents in early childhood during the first year of life. It is inherited in an autosomal-dominant pattern. Skin is dry and scaly over the entire body, although the antecubital and popliteal fossa may be uninvolved. The scalp may be involved as well. Atopy and keratosis pilaris may be associated. By adulthood, symptoms tend to abate.

Dr. Bilu Martin is a board-certified dermatologist in private practice at Premier Dermatology, MD, in Aventura, Fla. More diagnostic cases are available at mdedge.com/edermatologynews.com. To submit a case for possible publication, send an email to [email protected].

Ichthyosis vulgaris
 

Ichthyoses describe a group of disorders of cornification in which the epidermis differentiates abnormally, leading to generalized scaling of the skin. Ichthyosis is derived from the Greek word for fish, “ichthys.” Ichthyosis vulgaris is the most common of these conditions and often presents in early childhood during the first year of life. It is inherited in an autosomal-dominant pattern. Skin is dry and scaly over the entire body, although the antecubital and popliteal fossa may be uninvolved. The scalp may be involved as well. Atopy and keratosis pilaris may be associated. By adulthood, symptoms tend to abate.

Dr. Bilu Martin is a board-certified dermatologist in private practice at Premier Dermatology, MD, in Aventura, Fla. More diagnostic cases are available at mdedge.com/edermatologynews.com. To submit a case for possible publication, send an email to [email protected].

The Congo Red Dot Rapid Paper Test is superior both at detecting preeclampsia and at ruling it out, compared with conventional urine or serum testing, according to a research letter in EClinicalMedicine.

The research team, led by Kara M. Rood, MD, of the department of obstetrics and gynecology at the Ohio State University, Columbus, said that their pragmatic study in 346 consecutive pregnant patients demonstrated that the test is not only inexpensive, but also easy to use and well received by the nursing staff. A positive Congo Red Dot Rapid Paper Test had 80% sensitivity, 89% specificity, 92% negative predictive value and 87% accuracy to correctly diagnose preeclampsia.

The patients were recruited from the labor and delivery triage unit at the Ohio State University Wexner Medical Center. Certain misfolded proteins typically are found in the urine of women with preeclampsia, so in prior research, the researchers had hypothesized that a urine test that could detect these proteins would carry “diagnostic and prognostic potential for” preeclampsia. The researchers were able to show that this was possible with a laboratory test that used Congo Red dye because those misfolded proteins bind with it. This current study explored the accuracy of a 3-minute, point-of-care urine test that uses a dot of Congo Red dye on a piece of paper.

Other serum and urine tests, which often have been more complicated or time intensive, have failed to gain traction in real-world practice, as well as in low-resource countries where mortality and morbidity from preeclampsia are highest, the authors noted. By contrast, the researchers hope the rapid paper test they studied in the current research will fulfill that unmet need.

The study was funded by the Saving Lives at Birth grant and a grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

SOURCE: Rood KM et al. EClinicalMedicine. 2019. doi: 10.1016/j.eclinm.2019.02.004.
 

The Congo Red Dot Rapid Paper Test is superior both at detecting preeclampsia and at ruling it out, compared with conventional urine or serum testing, according to a research letter in EClinicalMedicine.

The research team, led by Kara M. Rood, MD, of the department of obstetrics and gynecology at the Ohio State University, Columbus, said that their pragmatic study in 346 consecutive pregnant patients demonstrated that the test is not only inexpensive, but also easy to use and well received by the nursing staff. A positive Congo Red Dot Rapid Paper Test had 80% sensitivity, 89% specificity, 92% negative predictive value and 87% accuracy to correctly diagnose preeclampsia.

The patients were recruited from the labor and delivery triage unit at the Ohio State University Wexner Medical Center. Certain misfolded proteins typically are found in the urine of women with preeclampsia, so in prior research, the researchers had hypothesized that a urine test that could detect these proteins would carry “diagnostic and prognostic potential for” preeclampsia. The researchers were able to show that this was possible with a laboratory test that used Congo Red dye because those misfolded proteins bind with it. This current study explored the accuracy of a 3-minute, point-of-care urine test that uses a dot of Congo Red dye on a piece of paper.

Other serum and urine tests, which often have been more complicated or time intensive, have failed to gain traction in real-world practice, as well as in low-resource countries where mortality and morbidity from preeclampsia are highest, the authors noted. By contrast, the researchers hope the rapid paper test they studied in the current research will fulfill that unmet need.

The study was funded by the Saving Lives at Birth grant and a grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

SOURCE: Rood KM et al. EClinicalMedicine. 2019. doi: 10.1016/j.eclinm.2019.02.004.
 

The Congo Red Dot Rapid Paper Test is superior both at detecting preeclampsia and at ruling it out, compared with conventional urine or serum testing, according to a research letter in EClinicalMedicine.

The research team, led by Kara M. Rood, MD, of the department of obstetrics and gynecology at the Ohio State University, Columbus, said that their pragmatic study in 346 consecutive pregnant patients demonstrated that the test is not only inexpensive, but also easy to use and well received by the nursing staff. A positive Congo Red Dot Rapid Paper Test had 80% sensitivity, 89% specificity, 92% negative predictive value and 87% accuracy to correctly diagnose preeclampsia.

The patients were recruited from the labor and delivery triage unit at the Ohio State University Wexner Medical Center. Certain misfolded proteins typically are found in the urine of women with preeclampsia, so in prior research, the researchers had hypothesized that a urine test that could detect these proteins would carry “diagnostic and prognostic potential for” preeclampsia. The researchers were able to show that this was possible with a laboratory test that used Congo Red dye because those misfolded proteins bind with it. This current study explored the accuracy of a 3-minute, point-of-care urine test that uses a dot of Congo Red dye on a piece of paper.

Other serum and urine tests, which often have been more complicated or time intensive, have failed to gain traction in real-world practice, as well as in low-resource countries where mortality and morbidity from preeclampsia are highest, the authors noted. By contrast, the researchers hope the rapid paper test they studied in the current research will fulfill that unmet need.

The study was funded by the Saving Lives at Birth grant and a grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

SOURCE: Rood KM et al. EClinicalMedicine. 2019. doi: 10.1016/j.eclinm.2019.02.004.
 

“X marks the spot!”

It’s one of the classic pirate tropes, bringing to mind images of Long John Silver, buried treasure, and a secret map with an “X” to show the hidden gold.

Brian Jackson/iStockphoto

Today that “X” (or, in some cases, a check mark or radio button) seems to be indicating where the money is to be lost, rather than found.

Hospital computer systems are increasingly reliant on preprogrammed order lists that you check off rather than the actual test itself. We’ve gone from having to write out the tests we want, to typing them into a box, to checking them off with a mouse.

I’ve seen systems where you’re offered a menu such as:

A. Brain MRI (noncontrast)

B. Brain MRI (w/wo contrast)

C. Head MRA (noncontrast)

D. Head MRA (with contrast)

E. Neck MRA (noncontrast)

F. Neck MRA (with contrast)

G. Brain MRI and head/neck MRA (noncontrast)

H. Brain MRI and head/neck MRA (w/wo contrast)

And that’s just for the brain and its vascular supply. Expand that to the rest of the nervous system, then to the whole body, then to other tests (labs) ... and you get the idea.

I suppose the driving force here is to make the system easier to use. Doctors are busy. It saves time just have to check a box if you want three tests, rather than note all of them individually.

But it’s really not that hard to check off three. Probably less than 5 seconds (as of my last time on call). And this is where, to me, X marks the spot where the money isn’t.

Humans, like most animals, are pretty good at defaulting to a low-energy setting. So if you only have to check off one box instead of three, or five, or whatever, why bother?

If the patient is being admitted for a stroke/TIA, then it makes sense to do the brain MRI and head/neck MRA. But what if it’s just headaches, or a new seizure, or a concussion? I see plenty of times when more tests are done than necessary, simply because the ordering physician either didn’t know what was really needed or because it was easier to just check the box.

This is not, in my experience, rare. I’d say anywhere from one-third to half of patients I’ve consulted on had an overkill neurological work-up, in which tests with no medical indications had been ordered. They’ve generally already been put in the system, or even done, before I get to the bedside.

I suppose one could say they should wait for the specialist to get there before any of the costly tests are ordered, but that opens up another can of worms. What if a critical finding that needed to be acted upon isn’t found in time because of such a rule? Not only that, but waiting for me to show up and order tests means it will take longer to get them done, adding onto the hospital stay, and (again) running up costs.

So that’s not an answer, either. There really isn’t one, unfortunately.

But, in our haste to make things easier, or faster, or even just flashier, the trend seems to be at the cost of doing things reasonably. At the same time that we’re trying to save money, the single “X” may be marking the spot where we’re actually throwing it away.

Dr. Block has a solo neurology practice in Scottsdale, Ariz.

“X marks the spot!”

It’s one of the classic pirate tropes, bringing to mind images of Long John Silver, buried treasure, and a secret map with an “X” to show the hidden gold.

Brian Jackson/iStockphoto

Today that “X” (or, in some cases, a check mark or radio button) seems to be indicating where the money is to be lost, rather than found.

Hospital computer systems are increasingly reliant on preprogrammed order lists that you check off rather than the actual test itself. We’ve gone from having to write out the tests we want, to typing them into a box, to checking them off with a mouse.

I’ve seen systems where you’re offered a menu such as:

A. Brain MRI (noncontrast)

B. Brain MRI (w/wo contrast)

C. Head MRA (noncontrast)

D. Head MRA (with contrast)

E. Neck MRA (noncontrast)

F. Neck MRA (with contrast)

G. Brain MRI and head/neck MRA (noncontrast)

H. Brain MRI and head/neck MRA (w/wo contrast)

And that’s just for the brain and its vascular supply. Expand that to the rest of the nervous system, then to the whole body, then to other tests (labs) ... and you get the idea.

I suppose the driving force here is to make the system easier to use. Doctors are busy. It saves time just have to check a box if you want three tests, rather than note all of them individually.

But it’s really not that hard to check off three. Probably less than 5 seconds (as of my last time on call). And this is where, to me, X marks the spot where the money isn’t.

Humans, like most animals, are pretty good at defaulting to a low-energy setting. So if you only have to check off one box instead of three, or five, or whatever, why bother?

If the patient is being admitted for a stroke/TIA, then it makes sense to do the brain MRI and head/neck MRA. But what if it’s just headaches, or a new seizure, or a concussion? I see plenty of times when more tests are done than necessary, simply because the ordering physician either didn’t know what was really needed or because it was easier to just check the box.

This is not, in my experience, rare. I’d say anywhere from one-third to half of patients I’ve consulted on had an overkill neurological work-up, in which tests with no medical indications had been ordered. They’ve generally already been put in the system, or even done, before I get to the bedside.

I suppose one could say they should wait for the specialist to get there before any of the costly tests are ordered, but that opens up another can of worms. What if a critical finding that needed to be acted upon isn’t found in time because of such a rule? Not only that, but waiting for me to show up and order tests means it will take longer to get them done, adding onto the hospital stay, and (again) running up costs.

So that’s not an answer, either. There really isn’t one, unfortunately.

But, in our haste to make things easier, or faster, or even just flashier, the trend seems to be at the cost of doing things reasonably. At the same time that we’re trying to save money, the single “X” may be marking the spot where we’re actually throwing it away.

Dr. Block has a solo neurology practice in Scottsdale, Ariz.

“X marks the spot!”

It’s one of the classic pirate tropes, bringing to mind images of Long John Silver, buried treasure, and a secret map with an “X” to show the hidden gold.

Brian Jackson/iStockphoto

Today that “X” (or, in some cases, a check mark or radio button) seems to be indicating where the money is to be lost, rather than found.

Hospital computer systems are increasingly reliant on preprogrammed order lists that you check off rather than the actual test itself. We’ve gone from having to write out the tests we want, to typing them into a box, to checking them off with a mouse.

I’ve seen systems where you’re offered a menu such as:

A. Brain MRI (noncontrast)

B. Brain MRI (w/wo contrast)

C. Head MRA (noncontrast)

D. Head MRA (with contrast)

E. Neck MRA (noncontrast)

F. Neck MRA (with contrast)

G. Brain MRI and head/neck MRA (noncontrast)

H. Brain MRI and head/neck MRA (w/wo contrast)

And that’s just for the brain and its vascular supply. Expand that to the rest of the nervous system, then to the whole body, then to other tests (labs) ... and you get the idea.

I suppose the driving force here is to make the system easier to use. Doctors are busy. It saves time just have to check a box if you want three tests, rather than note all of them individually.

But it’s really not that hard to check off three. Probably less than 5 seconds (as of my last time on call). And this is where, to me, X marks the spot where the money isn’t.

Humans, like most animals, are pretty good at defaulting to a low-energy setting. So if you only have to check off one box instead of three, or five, or whatever, why bother?

If the patient is being admitted for a stroke/TIA, then it makes sense to do the brain MRI and head/neck MRA. But what if it’s just headaches, or a new seizure, or a concussion? I see plenty of times when more tests are done than necessary, simply because the ordering physician either didn’t know what was really needed or because it was easier to just check the box.

This is not, in my experience, rare. I’d say anywhere from one-third to half of patients I’ve consulted on had an overkill neurological work-up, in which tests with no medical indications had been ordered. They’ve generally already been put in the system, or even done, before I get to the bedside.

I suppose one could say they should wait for the specialist to get there before any of the costly tests are ordered, but that opens up another can of worms. What if a critical finding that needed to be acted upon isn’t found in time because of such a rule? Not only that, but waiting for me to show up and order tests means it will take longer to get them done, adding onto the hospital stay, and (again) running up costs.

So that’s not an answer, either. There really isn’t one, unfortunately.

But, in our haste to make things easier, or faster, or even just flashier, the trend seems to be at the cost of doing things reasonably. At the same time that we’re trying to save money, the single “X” may be marking the spot where we’re actually throwing it away.

Dr. Block has a solo neurology practice in Scottsdale, Ariz.

In recent years, hospitals have made considerable efforts to improve transitions of care, in part due to financial incentives from the Medicare Hospital Readmission Reduction Program (HRRP).¹ Initially focusing on three medical conditions, the HRRP has been associated with significant reductions in readmission rates.² Importantly, a small proportion of patients accounts for a very large proportion of hospital readmissions and hospital use.^3,4 Frequently hospitalized patients often have multiple chronic conditions and unique needs which may not be met by conventional approaches to healthcare delivery, including those influenced by the HRRP.^4-6 In light of this challenge, some hospitals have developed programs specifically focused on frequently hospitalized patients. A recent systematic review of these programs found relatively few studies of high quality, providing only limited insight in designing interventions to support this population.⁷ Moreover, no studies appear to have incorporated the patients’ perspectives into the design or adaptation of the model. Members of our research team developed and implemented the Complex High Admission Management Program (CHAMP) in January 2016 to address the needs of frequently hospitalized patients in our hospital. To enhance CHAMP and inform the design of programs serving similar populations in other health systems, we sought to identify factors associated with the onset and continuation of high hospital use. Our research question was, from the patients’ perspective, what factors contribute to patients’ becoming and continuing to be high users of hospital care.

Setting, Study Design, and Participants

This qualitative study took place at Northwestern Memorial Hospital (NMH), an 894-bed urban academic hospital located in Chicago, Illinois. Between December 2016 and September 2017, we recruited adult patients admitted to the general medicine services. Eligible participants were identified with the assistance of a daily Northwestern Medicine Electronic Data Warehouse (EDW) search and included patients with two unplanned 30-day inpatient readmissions to NMH within the prior 12 months, in addition to one or more of the following criteria: (1) at least one readmission in the last six months; (2) a referral from one of the patient’s medical providers; or (3) at least three observation visits. We excluded patients whose preferred language was not English and those disoriented to person, place, or time. Considering NMH data showing that approximately one-third of high-utilizer patients have sickle cell disease, we used purposive sampling with the goal to compare findings within and between two groups of participants; those with and those without sickle cell disease. Our study was deemed exempt by the Northwestern University Institutional Review Board.

Participant Enrollment and Data Collection

We created an interview guide based on the research team’s experience with this population, a literature review, and our research question (See Appendix).^8,9 A research coordinator approached eligible participants during their hospital stay. The coordinator explained the study to eligible participants and obtained verbal consent for participation. The research coordinator then conducted one-on-one semi-structured interviews. Interviews were audio recorded for subsequent transcription and coding. Each interview lasted approximately 45 minutes. Participants were compensated with a $20 gift card for their time.

Analysis

Digital audio recordings from interviews were transcribed verbatim, deidentified, and analyzed using an iterative inductive team-based approach to coding.¹⁰ In our first cycle coding, all coders (KJO, SF, MMC, LO, KAC) independently reviewed and coded three transcripts using descriptive coding and subcoding to generate a preliminary codebook with code definitions.^10,11 Following the meetings to compare and compile our initial coding, each researcher then independently recoded the three transcripts with the developed codebook. The researchers met again to triangulate perspectives and reach a consensus on the final codebook. Using multiple coders is a standard process to control for subjective bias that one coder could bring to the coding process.¹² Following this meeting, the coders split into two teams of two (KJO, SF, and MMC, LO) to complete the coding of the remaining transcripts. Each team member independently coded the assigned transcripts and reconciled their codes with their counterpart; any discrepancies were resolved through discussion. Using this strategy, every transcript was coded by at least two team members. Our second coding cycle utilized pattern coding and involved identifying consistency both within and between transcripts; discovering associations between codes.^10,11,13 Constant comparison was used to compare responses among all participants, as well as between sickle-cell and nonsickle-cell participants.^13,14 Following team coding and reconciling, the analyses were presented to a broader research team for additional feedback and critique. All analyses were conducted using Dedoose version 8.0.35 (Los Angeles, California). Participant recruitment, interviews, and analysis of the transcripts continued until no new codes emerged and thematic saturation was achieved.

Participant Characteristics

Overall, we invited 34 patients to be interviewed; 26 consented and completed interviews (76.5%). Six (17.6%) patients declined participation, one (2.9%) was unable to complete the interview before hospital discharge, and one (2.9%) was excluded due to disorientation. Demographic characteristics of the 26 participants are shown in Table 1.

Four main themes emerged from our analysis. Table 2 summarizes these themes, subthemes, and provides representative quotes.

Major Medical Problem(s) are Universal, but High Hospital Use Varies in Onset

Not surprisingly, all participants described having at least one major medical problem. Some participants, such as those with genetic disorders, had experienced periods of high hospital use throughout their entire lifetime, while other participants experienced an onset of high hospital use as an adult after being previously healthy. Though most participants with genetic disorders had sickle cell anemia; one had a rare genetic disorder which caused chronic gastrointestinal symptoms. Participants typically described having a significant medical condition as well as other medical problems or complications from past surgery. Some participants described having a major medical problem which did not require frequent hospitalization until a complication or other medical problem arose, suggesting these new issues pushed them over a threshold beyond which self-management at home was less successful.

Course Fluctuates over Time and is Related to Psychological, Social, and Economic Factors

Participants identified psychological stress, social support, and financial constraints as factors which influence the course of their illness over time. Deaths in the family, breakups, and concerns about other family members were mentioned as specific forms of psychological stress and directly linked by participants to worsening of symptoms. Social support was present for most, but not all, participants, with no appreciable difference based on whether the participant had sickle cell disease. Social support was generally perceived as helpful, and several participants indicated a benefit to their own health when providing social support to others. Financial pressures also served as stressors and often impeded care due to lack of access to medications, other treatments, and housing.

Onset and Progression of Episodes Vary, but Generally Seem Uncontrollable

Regarding the onset of illness episodes, some participants described the sudden, unpredictable onset of symptoms, others described a more gradual onset which allowed them to attempt self-management. Regardless of the timing, episodes of illness were often perceived as spontaneous or triggered by factors outside of the participant’s control. Several participants, especially those with sickle cell disease, mentioned a relationship between their symptoms and the weather. Participants also noted the inconsistency in factors which may trigger an episode (ie, sometimes the factor exacerbated symptoms, while other times it did not). Participants also described having a high symptom burden with significant limitations in activities of daily living during episodes of illness. Pain was a very common component of symptoms regardless of whether or not the participant had sickle cell disease.

Individuals Seek Care after Self-Management Fails and Prefer to Avoid Hospitalization

Participants tried to control their symptoms with medications and typically sought care only when it was clear that this approach was not working, or they ran out of medications. This finding was consistent across both groups of participants (ie, those with and those without sickle cell disease). Many participants described very strong preferences not to come to the hospital; no participant described being in the hospital as a favorable or positive experience. Some participants mentioned that they had spent major holidays in the hospital and that they missed their family. No participant had a desire to come to the hospital.

In this study of frequently hospitalized patients, we found four major themes that illuminate patient perspectives about factors that contribute to high hospital use. While some of our findings corroborate those of previous studies, other emerging patterns were novel. Herein, we summarize key findings, provide context, and describe implications for the design of models of care for frequently hospitalized patients.

Similar to the findings of previous quantitative research, participants in our study described having a significant medical condition and typically had multiple medical conditions or complications.^4-6 Importantly, some participants described having a major medical problem which did not require frequent hospitalization until another medical problem or complication arose. This finding suggests that there may be an opportunity to identify patients with significant medical problems who are at elevated risk before the onset of high hospital use. Early identification of these high-risk patients could allow for the provision of additional support to prevent potential complications or address other factors which may contribute to the need for frequent hospitalization.

Participants in our study directly linked psychological stress to fluctuations in their course of illness. Previous research by Mautner and colleagues queried participants about childhood experiences and early life stressors and reported that early life instabilities and traumas were prevalent among patients with high levels of emergency and hospital-based healthcare utilization.¹⁵ Our participants identified more recent traumatic events (eg, the death of a loved one and breakups) when reflecting on factors contributing to illness exacerbations; early life trauma did not emerge as an identified contributor. Of note, unlike Mautner et al., we did not ask participants to reflect on childhood determinants of disease and illness specifically. Our findings suggest that psychological stress contributes to illness exacerbation, even for those patients without other significant psychiatric conditions (eg, depressive disorder, schizophrenia). Incorporating mental health professionals into programs for this patient population may improve health by teaching specific coping strategies, including cognitive-behavioral therapy for an acute stress disorder.^16,17

Social support was also a factor related to illness fluctuations over time. Notably, several participants indicated a benefit to their own health when providing social support to others, suggesting a role for peer support that may be reciprocally beneficial. This approach is supported by the literature. Williams and colleagues found that patients with sickle cell anemia experienced symptom improvement with peer support;¹⁸ while Johnson and colleagues recently reported a reduction in readmissions to acute care with the use of peer support for patients with severe mental illness.¹⁹

Financial constraints impeded care for some patients and served as a barrier to accessing medications, other treatments, and housing. Similar to the findings of prior quantitative research, our frequently hospitalized patients had a high proportion of patients with Medicaid and low proportion with private insurance, suggesting low socioeconomic status.^9,20 We did not formally collect data on income or economic status. Interestingly, prior qualitative studies have not identified financial constraints as a major theme, though this may be explained by differences in study populations and the overall objectives of the studies.^15,21 Importantly, the overwhelming majority of programs for frequently hospitalized patients identified in a recent systematic review included social workers.⁷ Our findings support the need to address financial constraints and the use of social workers in models of care for frequently hospitalized patients.

Many participants in our study felt that the factors contributing to exacerbations of illness were either inconsistent in their effect or out of their control. These findings have similarities to those from a qualitative study by Liu and colleagues in which they interviewed 20 “hospital-dependent” patients over 65 years of age.²¹ Though not explicitly focused on factors contributing to exacerbations, participants in their study felt that hospitalizations were generally inevitable. In our study, participants with sickle cell disease often identified changes in the weather as contributing to illness exacerbations. The relationship between weather and sickle cell disease remains incompletely understood, with an inconsistent association found in prior studies.²²

Participants in our study strongly desired to avoid hospitalization and typically sought hospital care when symptoms could not be controlled at home. This finding is in contrast to that from the study by Liu and colleagues where they found that hospital-dependent patients over 65 years had favorable perspectives of hospitalization because they felt safer and more secure in the hospital.²¹ Our participants were younger than those from the study by Liu and colleagues, had a high symptom burden, and may have been more concerned about control of those symptoms than the risk for clinical deterioration. Programs should aim to strengthen their support of patients’ self-management efforts early in the episode of illness and potentially offer home visits or a day hospital to avoid hospitalization. A recent systematic review found evidence that alternatives to inpatient care (eg, hospital-at-home) for low risk medical patients can achieve comparable outcomes at lower costs.²³ Similarly, some health systems have implemented day hospitals to treat low risk patients with uncomplicated sickle cell pain.^24,25

The heavy symptom burden experienced by participants in our study is notable. Pain was especially common. Programs may wish to partner with palliative care and addiction specialists to balance symptom relief with the simultaneous need to address comorbid substance and opioid use disorders when they are present.^4,9

Our study has several limitations. First, participants were recruited from the medicine service at a single academic hospital using criteria we developed to identify frequently hospitalized patients. Populations differ across hospitals and definitions of frequently hospitalized patients vary, limiting the generalizability of our findings. Second, we excluded patients whose preferred language was not English, as well as those disoriented to person, place, or time. It is possible that factors contributing to high hospital use differ for non-English speaking patients and those with cognitive deficits.

In this qualitative study, we identified factors associated with the onset and continuation of high hospital use. Emergent themes pointed to factors which influence patients’ onset of high hospital use, fluctuations in their illness over time, and triggers to seek care during an illness episode. These findings represent an important contribution to the literature because they allow patients’ perspectives to be incorporated into the design and adaptation of programs serving similar populations in other health systems. Programs that integrate patients’ perspectives into their design are likely to be better prepared to address patients’ needs and improve patient outcomes.

Acknowledgments

The authors thank the participants for their time and willingness to share their stories. The authors also thank Claire A. Knoten PhD and Erin Lambers PhD, former research team members who helped in the initial stages of the study.

Disclosures

The authors have nothing to disclose.

Funding

This project was funded by Northwestern Memorial Hospital and the Northwestern Medical Group.

In recent years, hospitals have made considerable efforts to improve transitions of care, in part due to financial incentives from the Medicare Hospital Readmission Reduction Program (HRRP).¹ Initially focusing on three medical conditions, the HRRP has been associated with significant reductions in readmission rates.² Importantly, a small proportion of patients accounts for a very large proportion of hospital readmissions and hospital use.^3,4 Frequently hospitalized patients often have multiple chronic conditions and unique needs which may not be met by conventional approaches to healthcare delivery, including those influenced by the HRRP.^4-6 In light of this challenge, some hospitals have developed programs specifically focused on frequently hospitalized patients. A recent systematic review of these programs found relatively few studies of high quality, providing only limited insight in designing interventions to support this population.⁷ Moreover, no studies appear to have incorporated the patients’ perspectives into the design or adaptation of the model. Members of our research team developed and implemented the Complex High Admission Management Program (CHAMP) in January 2016 to address the needs of frequently hospitalized patients in our hospital. To enhance CHAMP and inform the design of programs serving similar populations in other health systems, we sought to identify factors associated with the onset and continuation of high hospital use. Our research question was, from the patients’ perspective, what factors contribute to patients’ becoming and continuing to be high users of hospital care.

Setting, Study Design, and Participants

This qualitative study took place at Northwestern Memorial Hospital (NMH), an 894-bed urban academic hospital located in Chicago, Illinois. Between December 2016 and September 2017, we recruited adult patients admitted to the general medicine services. Eligible participants were identified with the assistance of a daily Northwestern Medicine Electronic Data Warehouse (EDW) search and included patients with two unplanned 30-day inpatient readmissions to NMH within the prior 12 months, in addition to one or more of the following criteria: (1) at least one readmission in the last six months; (2) a referral from one of the patient’s medical providers; or (3) at least three observation visits. We excluded patients whose preferred language was not English and those disoriented to person, place, or time. Considering NMH data showing that approximately one-third of high-utilizer patients have sickle cell disease, we used purposive sampling with the goal to compare findings within and between two groups of participants; those with and those without sickle cell disease. Our study was deemed exempt by the Northwestern University Institutional Review Board.

Participant Enrollment and Data Collection

We created an interview guide based on the research team’s experience with this population, a literature review, and our research question (See Appendix).^8,9 A research coordinator approached eligible participants during their hospital stay. The coordinator explained the study to eligible participants and obtained verbal consent for participation. The research coordinator then conducted one-on-one semi-structured interviews. Interviews were audio recorded for subsequent transcription and coding. Each interview lasted approximately 45 minutes. Participants were compensated with a $20 gift card for their time.

Analysis

Digital audio recordings from interviews were transcribed verbatim, deidentified, and analyzed using an iterative inductive team-based approach to coding.¹⁰ In our first cycle coding, all coders (KJO, SF, MMC, LO, KAC) independently reviewed and coded three transcripts using descriptive coding and subcoding to generate a preliminary codebook with code definitions.^10,11 Following the meetings to compare and compile our initial coding, each researcher then independently recoded the three transcripts with the developed codebook. The researchers met again to triangulate perspectives and reach a consensus on the final codebook. Using multiple coders is a standard process to control for subjective bias that one coder could bring to the coding process.¹² Following this meeting, the coders split into two teams of two (KJO, SF, and MMC, LO) to complete the coding of the remaining transcripts. Each team member independently coded the assigned transcripts and reconciled their codes with their counterpart; any discrepancies were resolved through discussion. Using this strategy, every transcript was coded by at least two team members. Our second coding cycle utilized pattern coding and involved identifying consistency both within and between transcripts; discovering associations between codes.^10,11,13 Constant comparison was used to compare responses among all participants, as well as between sickle-cell and nonsickle-cell participants.^13,14 Following team coding and reconciling, the analyses were presented to a broader research team for additional feedback and critique. All analyses were conducted using Dedoose version 8.0.35 (Los Angeles, California). Participant recruitment, interviews, and analysis of the transcripts continued until no new codes emerged and thematic saturation was achieved.

Participant Characteristics

Overall, we invited 34 patients to be interviewed; 26 consented and completed interviews (76.5%). Six (17.6%) patients declined participation, one (2.9%) was unable to complete the interview before hospital discharge, and one (2.9%) was excluded due to disorientation. Demographic characteristics of the 26 participants are shown in Table 1.

Four main themes emerged from our analysis. Table 2 summarizes these themes, subthemes, and provides representative quotes.

Major Medical Problem(s) are Universal, but High Hospital Use Varies in Onset

Not surprisingly, all participants described having at least one major medical problem. Some participants, such as those with genetic disorders, had experienced periods of high hospital use throughout their entire lifetime, while other participants experienced an onset of high hospital use as an adult after being previously healthy. Though most participants with genetic disorders had sickle cell anemia; one had a rare genetic disorder which caused chronic gastrointestinal symptoms. Participants typically described having a significant medical condition as well as other medical problems or complications from past surgery. Some participants described having a major medical problem which did not require frequent hospitalization until a complication or other medical problem arose, suggesting these new issues pushed them over a threshold beyond which self-management at home was less successful.

Course Fluctuates over Time and is Related to Psychological, Social, and Economic Factors

Participants identified psychological stress, social support, and financial constraints as factors which influence the course of their illness over time. Deaths in the family, breakups, and concerns about other family members were mentioned as specific forms of psychological stress and directly linked by participants to worsening of symptoms. Social support was present for most, but not all, participants, with no appreciable difference based on whether the participant had sickle cell disease. Social support was generally perceived as helpful, and several participants indicated a benefit to their own health when providing social support to others. Financial pressures also served as stressors and often impeded care due to lack of access to medications, other treatments, and housing.

Onset and Progression of Episodes Vary, but Generally Seem Uncontrollable

Regarding the onset of illness episodes, some participants described the sudden, unpredictable onset of symptoms, others described a more gradual onset which allowed them to attempt self-management. Regardless of the timing, episodes of illness were often perceived as spontaneous or triggered by factors outside of the participant’s control. Several participants, especially those with sickle cell disease, mentioned a relationship between their symptoms and the weather. Participants also noted the inconsistency in factors which may trigger an episode (ie, sometimes the factor exacerbated symptoms, while other times it did not). Participants also described having a high symptom burden with significant limitations in activities of daily living during episodes of illness. Pain was a very common component of symptoms regardless of whether or not the participant had sickle cell disease.

Individuals Seek Care after Self-Management Fails and Prefer to Avoid Hospitalization

Participants tried to control their symptoms with medications and typically sought care only when it was clear that this approach was not working, or they ran out of medications. This finding was consistent across both groups of participants (ie, those with and those without sickle cell disease). Many participants described very strong preferences not to come to the hospital; no participant described being in the hospital as a favorable or positive experience. Some participants mentioned that they had spent major holidays in the hospital and that they missed their family. No participant had a desire to come to the hospital.

In this study of frequently hospitalized patients, we found four major themes that illuminate patient perspectives about factors that contribute to high hospital use. While some of our findings corroborate those of previous studies, other emerging patterns were novel. Herein, we summarize key findings, provide context, and describe implications for the design of models of care for frequently hospitalized patients.

Similar to the findings of previous quantitative research, participants in our study described having a significant medical condition and typically had multiple medical conditions or complications.^4-6 Importantly, some participants described having a major medical problem which did not require frequent hospitalization until another medical problem or complication arose. This finding suggests that there may be an opportunity to identify patients with significant medical problems who are at elevated risk before the onset of high hospital use. Early identification of these high-risk patients could allow for the provision of additional support to prevent potential complications or address other factors which may contribute to the need for frequent hospitalization.

Participants in our study directly linked psychological stress to fluctuations in their course of illness. Previous research by Mautner and colleagues queried participants about childhood experiences and early life stressors and reported that early life instabilities and traumas were prevalent among patients with high levels of emergency and hospital-based healthcare utilization.¹⁵ Our participants identified more recent traumatic events (eg, the death of a loved one and breakups) when reflecting on factors contributing to illness exacerbations; early life trauma did not emerge as an identified contributor. Of note, unlike Mautner et al., we did not ask participants to reflect on childhood determinants of disease and illness specifically. Our findings suggest that psychological stress contributes to illness exacerbation, even for those patients without other significant psychiatric conditions (eg, depressive disorder, schizophrenia). Incorporating mental health professionals into programs for this patient population may improve health by teaching specific coping strategies, including cognitive-behavioral therapy for an acute stress disorder.^16,17

Social support was also a factor related to illness fluctuations over time. Notably, several participants indicated a benefit to their own health when providing social support to others, suggesting a role for peer support that may be reciprocally beneficial. This approach is supported by the literature. Williams and colleagues found that patients with sickle cell anemia experienced symptom improvement with peer support;¹⁸ while Johnson and colleagues recently reported a reduction in readmissions to acute care with the use of peer support for patients with severe mental illness.¹⁹

Financial constraints impeded care for some patients and served as a barrier to accessing medications, other treatments, and housing. Similar to the findings of prior quantitative research, our frequently hospitalized patients had a high proportion of patients with Medicaid and low proportion with private insurance, suggesting low socioeconomic status.^9,20 We did not formally collect data on income or economic status. Interestingly, prior qualitative studies have not identified financial constraints as a major theme, though this may be explained by differences in study populations and the overall objectives of the studies.^15,21 Importantly, the overwhelming majority of programs for frequently hospitalized patients identified in a recent systematic review included social workers.⁷ Our findings support the need to address financial constraints and the use of social workers in models of care for frequently hospitalized patients.

Many participants in our study felt that the factors contributing to exacerbations of illness were either inconsistent in their effect or out of their control. These findings have similarities to those from a qualitative study by Liu and colleagues in which they interviewed 20 “hospital-dependent” patients over 65 years of age.²¹ Though not explicitly focused on factors contributing to exacerbations, participants in their study felt that hospitalizations were generally inevitable. In our study, participants with sickle cell disease often identified changes in the weather as contributing to illness exacerbations. The relationship between weather and sickle cell disease remains incompletely understood, with an inconsistent association found in prior studies.²²

Participants in our study strongly desired to avoid hospitalization and typically sought hospital care when symptoms could not be controlled at home. This finding is in contrast to that from the study by Liu and colleagues where they found that hospital-dependent patients over 65 years had favorable perspectives of hospitalization because they felt safer and more secure in the hospital.²¹ Our participants were younger than those from the study by Liu and colleagues, had a high symptom burden, and may have been more concerned about control of those symptoms than the risk for clinical deterioration. Programs should aim to strengthen their support of patients’ self-management efforts early in the episode of illness and potentially offer home visits or a day hospital to avoid hospitalization. A recent systematic review found evidence that alternatives to inpatient care (eg, hospital-at-home) for low risk medical patients can achieve comparable outcomes at lower costs.²³ Similarly, some health systems have implemented day hospitals to treat low risk patients with uncomplicated sickle cell pain.^24,25

The heavy symptom burden experienced by participants in our study is notable. Pain was especially common. Programs may wish to partner with palliative care and addiction specialists to balance symptom relief with the simultaneous need to address comorbid substance and opioid use disorders when they are present.^4,9

Our study has several limitations. First, participants were recruited from the medicine service at a single academic hospital using criteria we developed to identify frequently hospitalized patients. Populations differ across hospitals and definitions of frequently hospitalized patients vary, limiting the generalizability of our findings. Second, we excluded patients whose preferred language was not English, as well as those disoriented to person, place, or time. It is possible that factors contributing to high hospital use differ for non-English speaking patients and those with cognitive deficits.

In this qualitative study, we identified factors associated with the onset and continuation of high hospital use. Emergent themes pointed to factors which influence patients’ onset of high hospital use, fluctuations in their illness over time, and triggers to seek care during an illness episode. These findings represent an important contribution to the literature because they allow patients’ perspectives to be incorporated into the design and adaptation of programs serving similar populations in other health systems. Programs that integrate patients’ perspectives into their design are likely to be better prepared to address patients’ needs and improve patient outcomes.

Acknowledgments

The authors thank the participants for their time and willingness to share their stories. The authors also thank Claire A. Knoten PhD and Erin Lambers PhD, former research team members who helped in the initial stages of the study.

Disclosures

The authors have nothing to disclose.

Funding

This project was funded by Northwestern Memorial Hospital and the Northwestern Medical Group.

In recent years, hospitals have made considerable efforts to improve transitions of care, in part due to financial incentives from the Medicare Hospital Readmission Reduction Program (HRRP).¹ Initially focusing on three medical conditions, the HRRP has been associated with significant reductions in readmission rates.² Importantly, a small proportion of patients accounts for a very large proportion of hospital readmissions and hospital use.^3,4 Frequently hospitalized patients often have multiple chronic conditions and unique needs which may not be met by conventional approaches to healthcare delivery, including those influenced by the HRRP.^4-6 In light of this challenge, some hospitals have developed programs specifically focused on frequently hospitalized patients. A recent systematic review of these programs found relatively few studies of high quality, providing only limited insight in designing interventions to support this population.⁷ Moreover, no studies appear to have incorporated the patients’ perspectives into the design or adaptation of the model. Members of our research team developed and implemented the Complex High Admission Management Program (CHAMP) in January 2016 to address the needs of frequently hospitalized patients in our hospital. To enhance CHAMP and inform the design of programs serving similar populations in other health systems, we sought to identify factors associated with the onset and continuation of high hospital use. Our research question was, from the patients’ perspective, what factors contribute to patients’ becoming and continuing to be high users of hospital care.

Setting, Study Design, and Participants

This qualitative study took place at Northwestern Memorial Hospital (NMH), an 894-bed urban academic hospital located in Chicago, Illinois. Between December 2016 and September 2017, we recruited adult patients admitted to the general medicine services. Eligible participants were identified with the assistance of a daily Northwestern Medicine Electronic Data Warehouse (EDW) search and included patients with two unplanned 30-day inpatient readmissions to NMH within the prior 12 months, in addition to one or more of the following criteria: (1) at least one readmission in the last six months; (2) a referral from one of the patient’s medical providers; or (3) at least three observation visits. We excluded patients whose preferred language was not English and those disoriented to person, place, or time. Considering NMH data showing that approximately one-third of high-utilizer patients have sickle cell disease, we used purposive sampling with the goal to compare findings within and between two groups of participants; those with and those without sickle cell disease. Our study was deemed exempt by the Northwestern University Institutional Review Board.

Participant Enrollment and Data Collection

We created an interview guide based on the research team’s experience with this population, a literature review, and our research question (See Appendix).^8,9 A research coordinator approached eligible participants during their hospital stay. The coordinator explained the study to eligible participants and obtained verbal consent for participation. The research coordinator then conducted one-on-one semi-structured interviews. Interviews were audio recorded for subsequent transcription and coding. Each interview lasted approximately 45 minutes. Participants were compensated with a $20 gift card for their time.

Analysis

Digital audio recordings from interviews were transcribed verbatim, deidentified, and analyzed using an iterative inductive team-based approach to coding.¹⁰ In our first cycle coding, all coders (KJO, SF, MMC, LO, KAC) independently reviewed and coded three transcripts using descriptive coding and subcoding to generate a preliminary codebook with code definitions.^10,11 Following the meetings to compare and compile our initial coding, each researcher then independently recoded the three transcripts with the developed codebook. The researchers met again to triangulate perspectives and reach a consensus on the final codebook. Using multiple coders is a standard process to control for subjective bias that one coder could bring to the coding process.¹² Following this meeting, the coders split into two teams of two (KJO, SF, and MMC, LO) to complete the coding of the remaining transcripts. Each team member independently coded the assigned transcripts and reconciled their codes with their counterpart; any discrepancies were resolved through discussion. Using this strategy, every transcript was coded by at least two team members. Our second coding cycle utilized pattern coding and involved identifying consistency both within and between transcripts; discovering associations between codes.^10,11,13 Constant comparison was used to compare responses among all participants, as well as between sickle-cell and nonsickle-cell participants.^13,14 Following team coding and reconciling, the analyses were presented to a broader research team for additional feedback and critique. All analyses were conducted using Dedoose version 8.0.35 (Los Angeles, California). Participant recruitment, interviews, and analysis of the transcripts continued until no new codes emerged and thematic saturation was achieved.

Participant Characteristics

Overall, we invited 34 patients to be interviewed; 26 consented and completed interviews (76.5%). Six (17.6%) patients declined participation, one (2.9%) was unable to complete the interview before hospital discharge, and one (2.9%) was excluded due to disorientation. Demographic characteristics of the 26 participants are shown in Table 1.

Four main themes emerged from our analysis. Table 2 summarizes these themes, subthemes, and provides representative quotes.

Major Medical Problem(s) are Universal, but High Hospital Use Varies in Onset

Not surprisingly, all participants described having at least one major medical problem. Some participants, such as those with genetic disorders, had experienced periods of high hospital use throughout their entire lifetime, while other participants experienced an onset of high hospital use as an adult after being previously healthy. Though most participants with genetic disorders had sickle cell anemia; one had a rare genetic disorder which caused chronic gastrointestinal symptoms. Participants typically described having a significant medical condition as well as other medical problems or complications from past surgery. Some participants described having a major medical problem which did not require frequent hospitalization until a complication or other medical problem arose, suggesting these new issues pushed them over a threshold beyond which self-management at home was less successful.

Course Fluctuates over Time and is Related to Psychological, Social, and Economic Factors

Participants identified psychological stress, social support, and financial constraints as factors which influence the course of their illness over time. Deaths in the family, breakups, and concerns about other family members were mentioned as specific forms of psychological stress and directly linked by participants to worsening of symptoms. Social support was present for most, but not all, participants, with no appreciable difference based on whether the participant had sickle cell disease. Social support was generally perceived as helpful, and several participants indicated a benefit to their own health when providing social support to others. Financial pressures also served as stressors and often impeded care due to lack of access to medications, other treatments, and housing.

Onset and Progression of Episodes Vary, but Generally Seem Uncontrollable

Regarding the onset of illness episodes, some participants described the sudden, unpredictable onset of symptoms, others described a more gradual onset which allowed them to attempt self-management. Regardless of the timing, episodes of illness were often perceived as spontaneous or triggered by factors outside of the participant’s control. Several participants, especially those with sickle cell disease, mentioned a relationship between their symptoms and the weather. Participants also noted the inconsistency in factors which may trigger an episode (ie, sometimes the factor exacerbated symptoms, while other times it did not). Participants also described having a high symptom burden with significant limitations in activities of daily living during episodes of illness. Pain was a very common component of symptoms regardless of whether or not the participant had sickle cell disease.

Individuals Seek Care after Self-Management Fails and Prefer to Avoid Hospitalization

Participants tried to control their symptoms with medications and typically sought care only when it was clear that this approach was not working, or they ran out of medications. This finding was consistent across both groups of participants (ie, those with and those without sickle cell disease). Many participants described very strong preferences not to come to the hospital; no participant described being in the hospital as a favorable or positive experience. Some participants mentioned that they had spent major holidays in the hospital and that they missed their family. No participant had a desire to come to the hospital.

In this study of frequently hospitalized patients, we found four major themes that illuminate patient perspectives about factors that contribute to high hospital use. While some of our findings corroborate those of previous studies, other emerging patterns were novel. Herein, we summarize key findings, provide context, and describe implications for the design of models of care for frequently hospitalized patients.

Similar to the findings of previous quantitative research, participants in our study described having a significant medical condition and typically had multiple medical conditions or complications.^4-6 Importantly, some participants described having a major medical problem which did not require frequent hospitalization until another medical problem or complication arose. This finding suggests that there may be an opportunity to identify patients with significant medical problems who are at elevated risk before the onset of high hospital use. Early identification of these high-risk patients could allow for the provision of additional support to prevent potential complications or address other factors which may contribute to the need for frequent hospitalization.

Participants in our study directly linked psychological stress to fluctuations in their course of illness. Previous research by Mautner and colleagues queried participants about childhood experiences and early life stressors and reported that early life instabilities and traumas were prevalent among patients with high levels of emergency and hospital-based healthcare utilization.¹⁵ Our participants identified more recent traumatic events (eg, the death of a loved one and breakups) when reflecting on factors contributing to illness exacerbations; early life trauma did not emerge as an identified contributor. Of note, unlike Mautner et al., we did not ask participants to reflect on childhood determinants of disease and illness specifically. Our findings suggest that psychological stress contributes to illness exacerbation, even for those patients without other significant psychiatric conditions (eg, depressive disorder, schizophrenia). Incorporating mental health professionals into programs for this patient population may improve health by teaching specific coping strategies, including cognitive-behavioral therapy for an acute stress disorder.^16,17

Social support was also a factor related to illness fluctuations over time. Notably, several participants indicated a benefit to their own health when providing social support to others, suggesting a role for peer support that may be reciprocally beneficial. This approach is supported by the literature. Williams and colleagues found that patients with sickle cell anemia experienced symptom improvement with peer support;¹⁸ while Johnson and colleagues recently reported a reduction in readmissions to acute care with the use of peer support for patients with severe mental illness.¹⁹

Financial constraints impeded care for some patients and served as a barrier to accessing medications, other treatments, and housing. Similar to the findings of prior quantitative research, our frequently hospitalized patients had a high proportion of patients with Medicaid and low proportion with private insurance, suggesting low socioeconomic status.^9,20 We did not formally collect data on income or economic status. Interestingly, prior qualitative studies have not identified financial constraints as a major theme, though this may be explained by differences in study populations and the overall objectives of the studies.^15,21 Importantly, the overwhelming majority of programs for frequently hospitalized patients identified in a recent systematic review included social workers.⁷ Our findings support the need to address financial constraints and the use of social workers in models of care for frequently hospitalized patients.

Many participants in our study felt that the factors contributing to exacerbations of illness were either inconsistent in their effect or out of their control. These findings have similarities to those from a qualitative study by Liu and colleagues in which they interviewed 20 “hospital-dependent” patients over 65 years of age.²¹ Though not explicitly focused on factors contributing to exacerbations, participants in their study felt that hospitalizations were generally inevitable. In our study, participants with sickle cell disease often identified changes in the weather as contributing to illness exacerbations. The relationship between weather and sickle cell disease remains incompletely understood, with an inconsistent association found in prior studies.²²

Participants in our study strongly desired to avoid hospitalization and typically sought hospital care when symptoms could not be controlled at home. This finding is in contrast to that from the study by Liu and colleagues where they found that hospital-dependent patients over 65 years had favorable perspectives of hospitalization because they felt safer and more secure in the hospital.²¹ Our participants were younger than those from the study by Liu and colleagues, had a high symptom burden, and may have been more concerned about control of those symptoms than the risk for clinical deterioration. Programs should aim to strengthen their support of patients’ self-management efforts early in the episode of illness and potentially offer home visits or a day hospital to avoid hospitalization. A recent systematic review found evidence that alternatives to inpatient care (eg, hospital-at-home) for low risk medical patients can achieve comparable outcomes at lower costs.²³ Similarly, some health systems have implemented day hospitals to treat low risk patients with uncomplicated sickle cell pain.^24,25

The heavy symptom burden experienced by participants in our study is notable. Pain was especially common. Programs may wish to partner with palliative care and addiction specialists to balance symptom relief with the simultaneous need to address comorbid substance and opioid use disorders when they are present.^4,9

Our study has several limitations. First, participants were recruited from the medicine service at a single academic hospital using criteria we developed to identify frequently hospitalized patients. Populations differ across hospitals and definitions of frequently hospitalized patients vary, limiting the generalizability of our findings. Second, we excluded patients whose preferred language was not English, as well as those disoriented to person, place, or time. It is possible that factors contributing to high hospital use differ for non-English speaking patients and those with cognitive deficits.

In this qualitative study, we identified factors associated with the onset and continuation of high hospital use. Emergent themes pointed to factors which influence patients’ onset of high hospital use, fluctuations in their illness over time, and triggers to seek care during an illness episode. These findings represent an important contribution to the literature because they allow patients’ perspectives to be incorporated into the design and adaptation of programs serving similar populations in other health systems. Programs that integrate patients’ perspectives into their design are likely to be better prepared to address patients’ needs and improve patient outcomes.

Acknowledgments

The authors thank the participants for their time and willingness to share their stories. The authors also thank Claire A. Knoten PhD and Erin Lambers PhD, former research team members who helped in the initial stages of the study.

Disclosures

The authors have nothing to disclose.

Funding

This project was funded by Northwestern Memorial Hospital and the Northwestern Medical Group.

Much has been written about the sources of the hidden curriculum in clerkships and postgraduate medical education.^1-3 However, these descriptions do not adequately account for the critical role that hospitalists play in the development of trainees when they encounter ethical challenges on teaching services.⁴ As a role model, teacher, and the attending of record, a hospitalist’s response to ethical issues in practice can have a pivotal influence on the life and work of trainees, either instilling positive virtues or perpetuating the negative impact of the hidden curriculum.^5-8 Understanding the epidemiology of ethical issues arising on academic hospitalist services has important implications for medical education, clinical ethics, and professionalism, as well as for patient care.

Study Setting and Design

We conducted a mixed-method observational study at NewYork–Presbyterian–Weill Cornell Medical Center, an 862-bed, tertiary-care, academic institution located in New York, New York. We performed a prospective description of the frequency of all consecutively identified ethical and contextual issues pertinent to clinical decision-making by observing morning rounds with housestaff hospitalist services. Ethical issues were categorized using a comprehensive standardized instrument previously developed and published by the Division of Medical Ethics.⁹

The Division of Hospital Medicine employs 79 physicians, 30 of whom are dedicated full-time to daytime care on house-staff (or teaching) or physician assistant services. Of these 30 physicians, two (7%) were coinvestigators in this project and were excluded from participation to avoid bias. Between September 2017 and May 2018, the attending physicians of record of all available housestaff services were invited to participate with their teams in our research study on a weekly basis. We observed 10 different Hospital Medicine attending physicians (10/28, 36% of the available physician sample) over 19 sessions. Before rounds, a brief introduction to the nature of the study was provided to each team. It was explicitly stated that the observers were present to identify and document possible ethical issues that may arise while discussing the patients on rounds, and that the purpose of the study was neither an evaluation of the team members or their decisions nor a critique or quality improvement exercise. Observing researchers were not allowed to participate in the discussion of any case.

To avoid potential case duplication, we allowed for a minimum two-week interval before rounding twice on any particular team. To control for interobserver variability, we observed in pairs during these sessions. Discrepancies between observers were resolved by post hoc discussion and application of the definitions of the standardized instrument used to identify and catalog ethical and contextual issues.

Study Variables and Definitions

The following variables were collected in all cases: observation date, name of reviewers, demographic characteristics of the patient (age, gender, race, ethnicity, marital status, religion, preferred language, insurance type, and living situation before the admission), patient’s location during the admission (emergency room, regular nursing floor, step-down unit, or other), and ethical and contextual issues. “Ethical issues” were defined as those situations involving a conflict of values or preferences among different stakeholders, including, but not limited to, providers, patients, and/or families. Explicit definitions of each issue were generated, and additional standard rules for completion were provided.

Statistical Analysis

Results are presented as n (%) or mean ± standard deviation. Percentages were rounded to the closest integer. Interobserver variability between the observers in relation to evaluating the presence or absence of ethical or contextual issues was assessed by the kappa statistic. All P values are two-sided, with statistical significance evaluated at the 0.05 alpha level. A 95% confidence interval (95% CI) for the kappa statistic (ie, for assessing interobserver variability) was calculated to assess the precision of the obtained kappa estimate. All analyses were performed in SAS Version 9.4 (SAS Institute, Inc., Cary, NC) and Stata Version 14.0 (StataCorp, College Station, TX).

General Characteristics of the Study Sample

In total, 270 patients were evaluated from the teaching hospitalist services during the observation period. Ethical issues were identified in 86 of these patients (31.8%). Observer ethicists disagreed in their initial evaluation of 17 cases (6.3%). After review of and adjudication, both observers agreed that nine of these 17 cases (3.3%) should be excluded from the final analysis, as none reached the necessary threshold to be considered as a true ethical issue. Hence, we report the results of 77 patients (28.5%). These cases comprised the Hospitalist group and involved 113 ethical issues (1.48 ± 0.5 ethical issues/case). Only five patients in the Hospitalist group had a formal clinical ethics consult before our observation (5/270 patients [1.9%] vs 77/270 patients [28.5%] with an ethical issue, respectively, P < .001). Although the majority of ethical issues were noted by members of the primary team (84%), 12 of the 77 cases in the Hospitalist group (16%) were identified only by the observing ethicists. The kappa statistic for interobserver variability between the observing ethicists was 0.85 (95% CI = 0.76-0.92). The major demographic characteristics are summarized in Table 1.

Ethical Challenges

The most common ethical issues hospitalists encountered involved discussions about goals of care (including decisions to pursue aggressive treatment versus hospice care, or debates about the team’s ambivalence about the benefits and risks of pursuing investigational chemotherapy), treatment refusals (including the decision to forgo biopsy of a suspected malignancy), or decision-making capacity (Table 2). Less common were issues pertaining to resource allocation (specially related to pressures to discharge patients), pain management (some patients were suspected of drug-seeking behavior), or surrogate decision-making (when alternative decision-makers were suspected to lack decision-making capacity). Discussions about forgoing life-sustaining treatments occurred only in four cases (5%). These involved considerations of withdrawing Bilevel Positive Airway Pressure (BiPAP), artificial nutrition and hydration, and/or stopping antibiotic treatment.

DISCUSSION

Our data are the first prospective description of ethical issues arising on an academic hospitalist teaching service. These results indicate that there is an ethics epidemiology in the routine practice of Hospital Medicine that has heretofore not been characterized. By this, we mean a discreet incidence and prevalence of ethical challenges in Hospital Medicine that is distinct from that which is encountered by clinical ethics consultation (CEC) services. Although most practitioners recognize the utility of a traditional ethics consultation, there is a surprising paucity of data about the sources of ethical conflict encountered by academic hospitalists at the bedside, particularly those addressed without CEC. This suggests that the criteria for requesting a formal ethics consult could be limited and restrictive, which is both undersensitive and overspecific.¹⁰ Because of these limitations, viewing traditional ethics consultation as a proxy for ethical issues arising in daily hospitalist practice would lead to an underestimation of the true prevalence, as our data indicate.

More than one-fourth of the patients admitted to hospitalist teaching services pose ethical conflicts. Some of these are addressed on rounds, some are not, and only a handful of these cases will ever be referred to an ethicist. CEC services are made aware of the “tip of the iceberg,” which accounts for a vanishingly small percentage of ethical issues that arise on daily rounds. Some hospitalists may not involve CEC simply because they believe that the services are not helpful. However, the failure to obtain consultation may also reflect an inability to recognize a “problematic situation” and formulate a referral that might benefit from the assistance of an ethics consultation.¹¹

Our study faces several potential limitations. We are presenting a single-center experience that focuses on the perspective of physicians and trainees. Some ethical issues might have been underestimated because the perspectives of patients, families, nurses, social workers, or other ancillary staff were not directly included. Furthermore, since any ethical challenge could have been discussed on any moment other than on morning rounds, our results may underestimate the prevalence of ethical issues arising from the hospital floors. Moreover, medical teams participating in the study could have been subject to the Hawthorne effect and could have tried to identify a greater number of ethical issues on rounds, which would not reflect actual practice.

Almost two decades ago, Coulehan and Williams wrote about the positive impact that ethics and humanities could have if these disciplines could be embedded in the daily practice of medicine, which is as follows:

…ethics and humanities curricula are irrelevant unless they can produce a substantive and continuing impact on hospital culture (…) The idea, of course, is to infiltrate the culture by coopting residents and attending physicians(…) If an ethics program can somehow achieve a critical mass of ‘‘value-sensitive’’ clinical faculty, it may begin to influence the institution’s ethos.¹²

Coulehan and Williams wrote of a need to bring ethics to the bedside. Our data suggest that an ethics epidemiology is deeply embedded in hospitalist services and is waiting to be fully characterized to better inform the care of patients and guide the professional formation and education of students and trainees. Hospitalists frequently confront ethical problems in daily practice that do not come to the attention of the CEC services or the institutional ethics committee. Understanding this emerging epidemiology presents an unrealized opportunity to improve bedside teaching, reinforce normative reasoning, and enhance patient care.

Acknowledgments

The authors want to acknowledge Drs. Augustine I. Choi, Michael G. Stewart, Laura L. Forese, and Anthony Hollenberg for their support of the fellowship in medical ethics and thank Drs. Arthur T. Evans and Monika M. Safford for their guidance.

Disclosures

The authors report no conflicts of interest.

Funding

This work was supported by a Weill Cornell General Internal Medicine Primary Care Innovations Initiative seed grant. Dr. Paul Christos was partially supported by the following grant: Clinical and Translational Science Center at Weill Cornell Medical College (1-UL1-TR002384-01).

Much has been written about the sources of the hidden curriculum in clerkships and postgraduate medical education.^1-3 However, these descriptions do not adequately account for the critical role that hospitalists play in the development of trainees when they encounter ethical challenges on teaching services.⁴ As a role model, teacher, and the attending of record, a hospitalist’s response to ethical issues in practice can have a pivotal influence on the life and work of trainees, either instilling positive virtues or perpetuating the negative impact of the hidden curriculum.^5-8 Understanding the epidemiology of ethical issues arising on academic hospitalist services has important implications for medical education, clinical ethics, and professionalism, as well as for patient care.

Study Setting and Design

We conducted a mixed-method observational study at NewYork–Presbyterian–Weill Cornell Medical Center, an 862-bed, tertiary-care, academic institution located in New York, New York. We performed a prospective description of the frequency of all consecutively identified ethical and contextual issues pertinent to clinical decision-making by observing morning rounds with housestaff hospitalist services. Ethical issues were categorized using a comprehensive standardized instrument previously developed and published by the Division of Medical Ethics.⁹

The Division of Hospital Medicine employs 79 physicians, 30 of whom are dedicated full-time to daytime care on house-staff (or teaching) or physician assistant services. Of these 30 physicians, two (7%) were coinvestigators in this project and were excluded from participation to avoid bias. Between September 2017 and May 2018, the attending physicians of record of all available housestaff services were invited to participate with their teams in our research study on a weekly basis. We observed 10 different Hospital Medicine attending physicians (10/28, 36% of the available physician sample) over 19 sessions. Before rounds, a brief introduction to the nature of the study was provided to each team. It was explicitly stated that the observers were present to identify and document possible ethical issues that may arise while discussing the patients on rounds, and that the purpose of the study was neither an evaluation of the team members or their decisions nor a critique or quality improvement exercise. Observing researchers were not allowed to participate in the discussion of any case.

To avoid potential case duplication, we allowed for a minimum two-week interval before rounding twice on any particular team. To control for interobserver variability, we observed in pairs during these sessions. Discrepancies between observers were resolved by post hoc discussion and application of the definitions of the standardized instrument used to identify and catalog ethical and contextual issues.

Study Variables and Definitions

The following variables were collected in all cases: observation date, name of reviewers, demographic characteristics of the patient (age, gender, race, ethnicity, marital status, religion, preferred language, insurance type, and living situation before the admission), patient’s location during the admission (emergency room, regular nursing floor, step-down unit, or other), and ethical and contextual issues. “Ethical issues” were defined as those situations involving a conflict of values or preferences among different stakeholders, including, but not limited to, providers, patients, and/or families. Explicit definitions of each issue were generated, and additional standard rules for completion were provided.

Statistical Analysis

Results are presented as n (%) or mean ± standard deviation. Percentages were rounded to the closest integer. Interobserver variability between the observers in relation to evaluating the presence or absence of ethical or contextual issues was assessed by the kappa statistic. All P values are two-sided, with statistical significance evaluated at the 0.05 alpha level. A 95% confidence interval (95% CI) for the kappa statistic (ie, for assessing interobserver variability) was calculated to assess the precision of the obtained kappa estimate. All analyses were performed in SAS Version 9.4 (SAS Institute, Inc., Cary, NC) and Stata Version 14.0 (StataCorp, College Station, TX).

General Characteristics of the Study Sample

In total, 270 patients were evaluated from the teaching hospitalist services during the observation period. Ethical issues were identified in 86 of these patients (31.8%). Observer ethicists disagreed in their initial evaluation of 17 cases (6.3%). After review of and adjudication, both observers agreed that nine of these 17 cases (3.3%) should be excluded from the final analysis, as none reached the necessary threshold to be considered as a true ethical issue. Hence, we report the results of 77 patients (28.5%). These cases comprised the Hospitalist group and involved 113 ethical issues (1.48 ± 0.5 ethical issues/case). Only five patients in the Hospitalist group had a formal clinical ethics consult before our observation (5/270 patients [1.9%] vs 77/270 patients [28.5%] with an ethical issue, respectively, P < .001). Although the majority of ethical issues were noted by members of the primary team (84%), 12 of the 77 cases in the Hospitalist group (16%) were identified only by the observing ethicists. The kappa statistic for interobserver variability between the observing ethicists was 0.85 (95% CI = 0.76-0.92). The major demographic characteristics are summarized in Table 1.

Ethical Challenges

The most common ethical issues hospitalists encountered involved discussions about goals of care (including decisions to pursue aggressive treatment versus hospice care, or debates about the team’s ambivalence about the benefits and risks of pursuing investigational chemotherapy), treatment refusals (including the decision to forgo biopsy of a suspected malignancy), or decision-making capacity (Table 2). Less common were issues pertaining to resource allocation (specially related to pressures to discharge patients), pain management (some patients were suspected of drug-seeking behavior), or surrogate decision-making (when alternative decision-makers were suspected to lack decision-making capacity). Discussions about forgoing life-sustaining treatments occurred only in four cases (5%). These involved considerations of withdrawing Bilevel Positive Airway Pressure (BiPAP), artificial nutrition and hydration, and/or stopping antibiotic treatment.

DISCUSSION

Our data are the first prospective description of ethical issues arising on an academic hospitalist teaching service. These results indicate that there is an ethics epidemiology in the routine practice of Hospital Medicine that has heretofore not been characterized. By this, we mean a discreet incidence and prevalence of ethical challenges in Hospital Medicine that is distinct from that which is encountered by clinical ethics consultation (CEC) services. Although most practitioners recognize the utility of a traditional ethics consultation, there is a surprising paucity of data about the sources of ethical conflict encountered by academic hospitalists at the bedside, particularly those addressed without CEC. This suggests that the criteria for requesting a formal ethics consult could be limited and restrictive, which is both undersensitive and overspecific.¹⁰ Because of these limitations, viewing traditional ethics consultation as a proxy for ethical issues arising in daily hospitalist practice would lead to an underestimation of the true prevalence, as our data indicate.

More than one-fourth of the patients admitted to hospitalist teaching services pose ethical conflicts. Some of these are addressed on rounds, some are not, and only a handful of these cases will ever be referred to an ethicist. CEC services are made aware of the “tip of the iceberg,” which accounts for a vanishingly small percentage of ethical issues that arise on daily rounds. Some hospitalists may not involve CEC simply because they believe that the services are not helpful. However, the failure to obtain consultation may also reflect an inability to recognize a “problematic situation” and formulate a referral that might benefit from the assistance of an ethics consultation.¹¹

Our study faces several potential limitations. We are presenting a single-center experience that focuses on the perspective of physicians and trainees. Some ethical issues might have been underestimated because the perspectives of patients, families, nurses, social workers, or other ancillary staff were not directly included. Furthermore, since any ethical challenge could have been discussed on any moment other than on morning rounds, our results may underestimate the prevalence of ethical issues arising from the hospital floors. Moreover, medical teams participating in the study could have been subject to the Hawthorne effect and could have tried to identify a greater number of ethical issues on rounds, which would not reflect actual practice.

Almost two decades ago, Coulehan and Williams wrote about the positive impact that ethics and humanities could have if these disciplines could be embedded in the daily practice of medicine, which is as follows:

…ethics and humanities curricula are irrelevant unless they can produce a substantive and continuing impact on hospital culture (…) The idea, of course, is to infiltrate the culture by coopting residents and attending physicians(…) If an ethics program can somehow achieve a critical mass of ‘‘value-sensitive’’ clinical faculty, it may begin to influence the institution’s ethos.¹²

Coulehan and Williams wrote of a need to bring ethics to the bedside. Our data suggest that an ethics epidemiology is deeply embedded in hospitalist services and is waiting to be fully characterized to better inform the care of patients and guide the professional formation and education of students and trainees. Hospitalists frequently confront ethical problems in daily practice that do not come to the attention of the CEC services or the institutional ethics committee. Understanding this emerging epidemiology presents an unrealized opportunity to improve bedside teaching, reinforce normative reasoning, and enhance patient care.

Acknowledgments

The authors want to acknowledge Drs. Augustine I. Choi, Michael G. Stewart, Laura L. Forese, and Anthony Hollenberg for their support of the fellowship in medical ethics and thank Drs. Arthur T. Evans and Monika M. Safford for their guidance.

Disclosures

The authors report no conflicts of interest.

Funding

This work was supported by a Weill Cornell General Internal Medicine Primary Care Innovations Initiative seed grant. Dr. Paul Christos was partially supported by the following grant: Clinical and Translational Science Center at Weill Cornell Medical College (1-UL1-TR002384-01).

Much has been written about the sources of the hidden curriculum in clerkships and postgraduate medical education.^1-3 However, these descriptions do not adequately account for the critical role that hospitalists play in the development of trainees when they encounter ethical challenges on teaching services.⁴ As a role model, teacher, and the attending of record, a hospitalist’s response to ethical issues in practice can have a pivotal influence on the life and work of trainees, either instilling positive virtues or perpetuating the negative impact of the hidden curriculum.^5-8 Understanding the epidemiology of ethical issues arising on academic hospitalist services has important implications for medical education, clinical ethics, and professionalism, as well as for patient care.

Study Setting and Design

We conducted a mixed-method observational study at NewYork–Presbyterian–Weill Cornell Medical Center, an 862-bed, tertiary-care, academic institution located in New York, New York. We performed a prospective description of the frequency of all consecutively identified ethical and contextual issues pertinent to clinical decision-making by observing morning rounds with housestaff hospitalist services. Ethical issues were categorized using a comprehensive standardized instrument previously developed and published by the Division of Medical Ethics.⁹

The Division of Hospital Medicine employs 79 physicians, 30 of whom are dedicated full-time to daytime care on house-staff (or teaching) or physician assistant services. Of these 30 physicians, two (7%) were coinvestigators in this project and were excluded from participation to avoid bias. Between September 2017 and May 2018, the attending physicians of record of all available housestaff services were invited to participate with their teams in our research study on a weekly basis. We observed 10 different Hospital Medicine attending physicians (10/28, 36% of the available physician sample) over 19 sessions. Before rounds, a brief introduction to the nature of the study was provided to each team. It was explicitly stated that the observers were present to identify and document possible ethical issues that may arise while discussing the patients on rounds, and that the purpose of the study was neither an evaluation of the team members or their decisions nor a critique or quality improvement exercise. Observing researchers were not allowed to participate in the discussion of any case.

To avoid potential case duplication, we allowed for a minimum two-week interval before rounding twice on any particular team. To control for interobserver variability, we observed in pairs during these sessions. Discrepancies between observers were resolved by post hoc discussion and application of the definitions of the standardized instrument used to identify and catalog ethical and contextual issues.

Study Variables and Definitions

The following variables were collected in all cases: observation date, name of reviewers, demographic characteristics of the patient (age, gender, race, ethnicity, marital status, religion, preferred language, insurance type, and living situation before the admission), patient’s location during the admission (emergency room, regular nursing floor, step-down unit, or other), and ethical and contextual issues. “Ethical issues” were defined as those situations involving a conflict of values or preferences among different stakeholders, including, but not limited to, providers, patients, and/or families. Explicit definitions of each issue were generated, and additional standard rules for completion were provided.

Statistical Analysis

Results are presented as n (%) or mean ± standard deviation. Percentages were rounded to the closest integer. Interobserver variability between the observers in relation to evaluating the presence or absence of ethical or contextual issues was assessed by the kappa statistic. All P values are two-sided, with statistical significance evaluated at the 0.05 alpha level. A 95% confidence interval (95% CI) for the kappa statistic (ie, for assessing interobserver variability) was calculated to assess the precision of the obtained kappa estimate. All analyses were performed in SAS Version 9.4 (SAS Institute, Inc., Cary, NC) and Stata Version 14.0 (StataCorp, College Station, TX).

General Characteristics of the Study Sample

In total, 270 patients were evaluated from the teaching hospitalist services during the observation period. Ethical issues were identified in 86 of these patients (31.8%). Observer ethicists disagreed in their initial evaluation of 17 cases (6.3%). After review of and adjudication, both observers agreed that nine of these 17 cases (3.3%) should be excluded from the final analysis, as none reached the necessary threshold to be considered as a true ethical issue. Hence, we report the results of 77 patients (28.5%). These cases comprised the Hospitalist group and involved 113 ethical issues (1.48 ± 0.5 ethical issues/case). Only five patients in the Hospitalist group had a formal clinical ethics consult before our observation (5/270 patients [1.9%] vs 77/270 patients [28.5%] with an ethical issue, respectively, P < .001). Although the majority of ethical issues were noted by members of the primary team (84%), 12 of the 77 cases in the Hospitalist group (16%) were identified only by the observing ethicists. The kappa statistic for interobserver variability between the observing ethicists was 0.85 (95% CI = 0.76-0.92). The major demographic characteristics are summarized in Table 1.

Ethical Challenges

The most common ethical issues hospitalists encountered involved discussions about goals of care (including decisions to pursue aggressive treatment versus hospice care, or debates about the team’s ambivalence about the benefits and risks of pursuing investigational chemotherapy), treatment refusals (including the decision to forgo biopsy of a suspected malignancy), or decision-making capacity (Table 2). Less common were issues pertaining to resource allocation (specially related to pressures to discharge patients), pain management (some patients were suspected of drug-seeking behavior), or surrogate decision-making (when alternative decision-makers were suspected to lack decision-making capacity). Discussions about forgoing life-sustaining treatments occurred only in four cases (5%). These involved considerations of withdrawing Bilevel Positive Airway Pressure (BiPAP), artificial nutrition and hydration, and/or stopping antibiotic treatment.

DISCUSSION

Our data are the first prospective description of ethical issues arising on an academic hospitalist teaching service. These results indicate that there is an ethics epidemiology in the routine practice of Hospital Medicine that has heretofore not been characterized. By this, we mean a discreet incidence and prevalence of ethical challenges in Hospital Medicine that is distinct from that which is encountered by clinical ethics consultation (CEC) services. Although most practitioners recognize the utility of a traditional ethics consultation, there is a surprising paucity of data about the sources of ethical conflict encountered by academic hospitalists at the bedside, particularly those addressed without CEC. This suggests that the criteria for requesting a formal ethics consult could be limited and restrictive, which is both undersensitive and overspecific.¹⁰ Because of these limitations, viewing traditional ethics consultation as a proxy for ethical issues arising in daily hospitalist practice would lead to an underestimation of the true prevalence, as our data indicate.

More than one-fourth of the patients admitted to hospitalist teaching services pose ethical conflicts. Some of these are addressed on rounds, some are not, and only a handful of these cases will ever be referred to an ethicist. CEC services are made aware of the “tip of the iceberg,” which accounts for a vanishingly small percentage of ethical issues that arise on daily rounds. Some hospitalists may not involve CEC simply because they believe that the services are not helpful. However, the failure to obtain consultation may also reflect an inability to recognize a “problematic situation” and formulate a referral that might benefit from the assistance of an ethics consultation.¹¹

Our study faces several potential limitations. We are presenting a single-center experience that focuses on the perspective of physicians and trainees. Some ethical issues might have been underestimated because the perspectives of patients, families, nurses, social workers, or other ancillary staff were not directly included. Furthermore, since any ethical challenge could have been discussed on any moment other than on morning rounds, our results may underestimate the prevalence of ethical issues arising from the hospital floors. Moreover, medical teams participating in the study could have been subject to the Hawthorne effect and could have tried to identify a greater number of ethical issues on rounds, which would not reflect actual practice.

Almost two decades ago, Coulehan and Williams wrote about the positive impact that ethics and humanities could have if these disciplines could be embedded in the daily practice of medicine, which is as follows:

…ethics and humanities curricula are irrelevant unless they can produce a substantive and continuing impact on hospital culture (…) The idea, of course, is to infiltrate the culture by coopting residents and attending physicians(…) If an ethics program can somehow achieve a critical mass of ‘‘value-sensitive’’ clinical faculty, it may begin to influence the institution’s ethos.¹²

Coulehan and Williams wrote of a need to bring ethics to the bedside. Our data suggest that an ethics epidemiology is deeply embedded in hospitalist services and is waiting to be fully characterized to better inform the care of patients and guide the professional formation and education of students and trainees. Hospitalists frequently confront ethical problems in daily practice that do not come to the attention of the CEC services or the institutional ethics committee. Understanding this emerging epidemiology presents an unrealized opportunity to improve bedside teaching, reinforce normative reasoning, and enhance patient care.

Acknowledgments

The authors want to acknowledge Drs. Augustine I. Choi, Michael G. Stewart, Laura L. Forese, and Anthony Hollenberg for their support of the fellowship in medical ethics and thank Drs. Arthur T. Evans and Monika M. Safford for their guidance.

Disclosures

The authors report no conflicts of interest.

Funding

This work was supported by a Weill Cornell General Internal Medicine Primary Care Innovations Initiative seed grant. Dr. Paul Christos was partially supported by the following grant: Clinical and Translational Science Center at Weill Cornell Medical College (1-UL1-TR002384-01).

Among Asian American subgroups, Asian Indian, Filipino, and Vietnamese populations showed significantly higher premature death rates from ischemic heart disease, compared with other Asian subgroups, based on data from the National Center for Health Statistics for the years 2003 to 2012.

Previous studies have described death rates from cardiovascular disease in Asian subgroups, but premature death in particular has not been well studied, wrote Latha Palaniappan, MD, of the division of primary care and population health at the Stanford (Calif.) University, and her colleagues.

To examine premature mortality from cardiovascular disease in Asian subgroups, the researchers used years of potential life lost (YPLL) to measure premature mortality. “[Years of potential life lost ] compares age at death with average life expectancy to estimate the average time an individual would have lived had he/she not died prematurely from a specific disease,” they explained.

The study population included 354,256 Asian American decedents aged 25 years or older. Of that total, 59,936 died of ischemic heart disease and 28,489 died of cerebrovascular disease.

Overall, Asian men lost 779 years/100,000 people in 2003 and 574 years/100,000 in 2012. However, in 2003, Asian Indian men in particular lost 1,216 years/100,000, more than other Asian male subgroups and non-Hispanic white men.

“Use of race-specific life expectancy revealed greater heterogeneity in YPLL across all Asian subgroups,” the researchers wrote. Similarly, Asian Indian women had the highest years of potential life lost throughout the study period, with a high of 818 years/100,000 people in 2003 and 477 years/100,00 in 2012, compared with 577/100,000 and 426/100,000, respectively, among non-Hispanic white women.

All Asian male subgroups also lost more years of life to cerebrovascular disease, compared with non-Hispanic white men, and women in each Asian subgroup had a higher years of potential life lost, compared with non-Hispanic white women. Filipino men had the highest YPLL values for the period, followed by Vietnamese men, and the patterns were similar for Filipino and Vietnamese women.

Possible explanations for the high rate of premature death from ischemic heart disease in Asian Indians include greater prevalence of risk factors at younger age (including elevated apolipoprotein B100/apolipoprotein A-1 ratios), type 2 diabetes, and cardiometabolic abnormalities in people of normal weight that might go unnoticed in a clinical exam, the researchers said. In the case of cerebrovascular disease, possible risk factors for high years of potential life lost in certain subgroups include hypertension in Filipino populations, limited health literacy about stroke in Vietnamese populations, and high rates of smoking in Vietnamese men.

The study findings were limited by several factors, including the small amount of data on mortality in Asian Americans from census reports, the researchers noted. However, the use of years of potential life lost as a measure of the impact of cardiovascular disease provided a useful model of the impact of cardiovascular disease on life expectancy and total disease burden of cerebrovascular disease on Asian ethnic subgroups, they said.

“Our study also provides evidence that evaluating the Asian population together as one group underestimates the burden of [cerebrovascular disease],” they noted.

The National Institute of Minority Health and Health Disparities Research Project and the National Heart, Lung, and Blood Institute supported the study in part by grants to researchers. The researchers had no financial conflicts to disclose.

SOURCE: Iyer DG et al. J Am Heart Assoc. 2019 Mar 20. doi: 10.1161/JAHA.118.010744.

Among Asian American subgroups, Asian Indian, Filipino, and Vietnamese populations showed significantly higher premature death rates from ischemic heart disease, compared with other Asian subgroups, based on data from the National Center for Health Statistics for the years 2003 to 2012.

Previous studies have described death rates from cardiovascular disease in Asian subgroups, but premature death in particular has not been well studied, wrote Latha Palaniappan, MD, of the division of primary care and population health at the Stanford (Calif.) University, and her colleagues.

To examine premature mortality from cardiovascular disease in Asian subgroups, the researchers used years of potential life lost (YPLL) to measure premature mortality. “[Years of potential life lost ] compares age at death with average life expectancy to estimate the average time an individual would have lived had he/she not died prematurely from a specific disease,” they explained.

The study population included 354,256 Asian American decedents aged 25 years or older. Of that total, 59,936 died of ischemic heart disease and 28,489 died of cerebrovascular disease.

Overall, Asian men lost 779 years/100,000 people in 2003 and 574 years/100,000 in 2012. However, in 2003, Asian Indian men in particular lost 1,216 years/100,000, more than other Asian male subgroups and non-Hispanic white men.

“Use of race-specific life expectancy revealed greater heterogeneity in YPLL across all Asian subgroups,” the researchers wrote. Similarly, Asian Indian women had the highest years of potential life lost throughout the study period, with a high of 818 years/100,000 people in 2003 and 477 years/100,00 in 2012, compared with 577/100,000 and 426/100,000, respectively, among non-Hispanic white women.

All Asian male subgroups also lost more years of life to cerebrovascular disease, compared with non-Hispanic white men, and women in each Asian subgroup had a higher years of potential life lost, compared with non-Hispanic white women. Filipino men had the highest YPLL values for the period, followed by Vietnamese men, and the patterns were similar for Filipino and Vietnamese women.

Possible explanations for the high rate of premature death from ischemic heart disease in Asian Indians include greater prevalence of risk factors at younger age (including elevated apolipoprotein B100/apolipoprotein A-1 ratios), type 2 diabetes, and cardiometabolic abnormalities in people of normal weight that might go unnoticed in a clinical exam, the researchers said. In the case of cerebrovascular disease, possible risk factors for high years of potential life lost in certain subgroups include hypertension in Filipino populations, limited health literacy about stroke in Vietnamese populations, and high rates of smoking in Vietnamese men.

The study findings were limited by several factors, including the small amount of data on mortality in Asian Americans from census reports, the researchers noted. However, the use of years of potential life lost as a measure of the impact of cardiovascular disease provided a useful model of the impact of cardiovascular disease on life expectancy and total disease burden of cerebrovascular disease on Asian ethnic subgroups, they said.

“Our study also provides evidence that evaluating the Asian population together as one group underestimates the burden of [cerebrovascular disease],” they noted.

The National Institute of Minority Health and Health Disparities Research Project and the National Heart, Lung, and Blood Institute supported the study in part by grants to researchers. The researchers had no financial conflicts to disclose.

SOURCE: Iyer DG et al. J Am Heart Assoc. 2019 Mar 20. doi: 10.1161/JAHA.118.010744.

Among Asian American subgroups, Asian Indian, Filipino, and Vietnamese populations showed significantly higher premature death rates from ischemic heart disease, compared with other Asian subgroups, based on data from the National Center for Health Statistics for the years 2003 to 2012.

Previous studies have described death rates from cardiovascular disease in Asian subgroups, but premature death in particular has not been well studied, wrote Latha Palaniappan, MD, of the division of primary care and population health at the Stanford (Calif.) University, and her colleagues.

To examine premature mortality from cardiovascular disease in Asian subgroups, the researchers used years of potential life lost (YPLL) to measure premature mortality. “[Years of potential life lost ] compares age at death with average life expectancy to estimate the average time an individual would have lived had he/she not died prematurely from a specific disease,” they explained.

The study population included 354,256 Asian American decedents aged 25 years or older. Of that total, 59,936 died of ischemic heart disease and 28,489 died of cerebrovascular disease.

Overall, Asian men lost 779 years/100,000 people in 2003 and 574 years/100,000 in 2012. However, in 2003, Asian Indian men in particular lost 1,216 years/100,000, more than other Asian male subgroups and non-Hispanic white men.

“Use of race-specific life expectancy revealed greater heterogeneity in YPLL across all Asian subgroups,” the researchers wrote. Similarly, Asian Indian women had the highest years of potential life lost throughout the study period, with a high of 818 years/100,000 people in 2003 and 477 years/100,00 in 2012, compared with 577/100,000 and 426/100,000, respectively, among non-Hispanic white women.

All Asian male subgroups also lost more years of life to cerebrovascular disease, compared with non-Hispanic white men, and women in each Asian subgroup had a higher years of potential life lost, compared with non-Hispanic white women. Filipino men had the highest YPLL values for the period, followed by Vietnamese men, and the patterns were similar for Filipino and Vietnamese women.

Possible explanations for the high rate of premature death from ischemic heart disease in Asian Indians include greater prevalence of risk factors at younger age (including elevated apolipoprotein B100/apolipoprotein A-1 ratios), type 2 diabetes, and cardiometabolic abnormalities in people of normal weight that might go unnoticed in a clinical exam, the researchers said. In the case of cerebrovascular disease, possible risk factors for high years of potential life lost in certain subgroups include hypertension in Filipino populations, limited health literacy about stroke in Vietnamese populations, and high rates of smoking in Vietnamese men.

The study findings were limited by several factors, including the small amount of data on mortality in Asian Americans from census reports, the researchers noted. However, the use of years of potential life lost as a measure of the impact of cardiovascular disease provided a useful model of the impact of cardiovascular disease on life expectancy and total disease burden of cerebrovascular disease on Asian ethnic subgroups, they said.

“Our study also provides evidence that evaluating the Asian population together as one group underestimates the burden of [cerebrovascular disease],” they noted.

The National Institute of Minority Health and Health Disparities Research Project and the National Heart, Lung, and Blood Institute supported the study in part by grants to researchers. The researchers had no financial conflicts to disclose.

SOURCE: Iyer DG et al. J Am Heart Assoc. 2019 Mar 20. doi: 10.1161/JAHA.118.010744.

Accountable care organizations (ACOs) create incentives for more efficient healthcare utilization. For patients being discharged from the hospital, this may mean more efficient use of postacute care (PAC), including discharging patients to higher quality skilled nursing facilities (SNFs) in an effort to limit readmissions and other costly complications. Public reporting of nursing home quality has been associated with improved performance measures, although improvements in preventable hospitalizations have lagged.¹ Evidence to date suggests that patients attributed to an ACO are not going to higher quality SNFs,^2,3 but these effects may be concentrated in hospitals that participate in ACOs and face stronger incentives to alter their discharge patterns compared with non-ACO hospitals. Therefore, we examined whether hospitals participating in Medicare’s Shared Saving Program (MSSP) increased the use of highly rated SNFs or decreased the use of low-rated SNFs hospital-wide after initiation of their ACO contracts compared with non-ACO hospitals.

We used discharge-level data from the 100% MedPAR file for all fee-for-service Medicare beneficiaries discharged from an acute care hospital to an SNF between 2010 and 2013. We measured the SNF quality using Medicare’s Nursing Home Compare star ratings. Our primary outcome was probability of discharge to high-rated (five star) and low-rated (one star) SNFs.

We utilized a difference-in-differences design. Using a linear probability model, we first estimated the change in the probability of discharge to five-star SNFs (compared to all other SNFs) among all beneficiaries discharged from one of the 233 ACO-participating hospitals after the hospital became an ACO provider compared with before and compared withall beneficiaries discharged from one of the 3,081 non-ACO hospitals over the same time period. Individual hospitals were determined to be “ACO-participating” if they were listed on Medicare’s website as being part of an ACO-participating hospital in the MSSP. ACOs joined the MSSP in three waves: April 1, 2012; July 1, 2012; and January 1, 2013, which were also determined based on information on Medicare’s website. We separately estimated the change in probability of discharge to a one-star SNF (compared to all other SNFs) using the same approach. Models were adjusted for beneficiary demographic and clinical characteristics (age, sex, race, dual eligibility, urban ZIP code, diagnosis-related group code, and Elixhauser comorbidities) and market characteristics (the concentration of hospital discharges, SNF discharges, and the number of five-star SNFs, all measured in each hospital referral region).

We examined a total of 12,736,287 discharges, 11.8% from ACO-participating hospitals and 88.2% from non-ACO-participating hospitals. ACO-participating hospitals cared for fewer black patients and fewer patients who were dually enrolled in Medicare and Medicaid (Table 1), but these characteristics did not change differentially between the two groups of hospitals over our study period. ACO-participating hospitals were also more likely to discharge patients to five-star SNFs prior to joining an ACO (in 2010-2011). After joining an ACO, the percentage of hospital discharges going to a 5-star SNF increased by 3.4 percentage points on a base of 15.4% (95% confidence interval [CI] 1.3-5.5, P = .002; Table 2) compared with non-ACO-participating hospitals over the same time period. The differential changes did not extend to SNFs rated as three stars and above (change of 0.5 percentage points, 95% CI, 1.3-2.8, P = .600).

The probability of discharge from an ACO hospital to low-quality (one-star) SNFs did not change significantly from its baseline level of 13.5% after joining an ACO compared with non-ACO-participating hospitals (change of 0.4 percentage points, 95% CI, 0.7-1.5, P = .494).

Our findings indicate that ACO-participating hospitals were more likely to discharge patients to the highest rated SNFs after they began their ACO contract but did not change the likelihood of discharge to lower rated SNFs in comparison with non-ACO hospitals. Previous research has suggested that patients attributed to a Medicare ACO were not more likely to use high-quality SNFs. However, we examined the effect of hospital participation in an ACO, not individual beneficiaries attributed to an ACO. These contrasting results suggest that hospitals could be instituting hospital-wide changes in discharge patterns once they join an ACO and that hospital-led ACOs could be particularly well positioned to manage postdischarge care relative to physician-led ACOs. One potential limitation of this study is that ACO-participating hospitals may differ in unobservable ways from non-ACO-participating hospitals. However, using hospital fixed effects, we mitigated this limitation to some extent by controlling for time-invariant observed and unobserved characteristics. Further work will need to explore the mechanisms of higher PAC quality, including hospital-SNF integration and coordination.

Disclosures

Dr. Werner reports receiving personal fees from CarePort Health. Dr. Bain reports no conflicts. Mr. Yuan reports no conflicts. Dr. Navathe reports receiving personal fees from Navvis and Company, Navigant Inc., Lynx Medical, Indegene Inc., Sutherland Global Services, and Agathos, Inc.; personal fees and equity from NavaHealth; an honorarium from Elsevier Press, serving on the board of Integrated Services, Inc. without compensation, and grants from Hawaii Medical Service Association, Anthem Public Policy Institute, and Oscar Health, none of which are related to this manuscript.

Funding

This research was funded by R01-HS024266 by the Agency for Healthcare Research and Quality. Rachel Werner was supported in part by K24-AG047908 from the National Institute on Aging.

Accountable care organizations (ACOs) create incentives for more efficient healthcare utilization. For patients being discharged from the hospital, this may mean more efficient use of postacute care (PAC), including discharging patients to higher quality skilled nursing facilities (SNFs) in an effort to limit readmissions and other costly complications. Public reporting of nursing home quality has been associated with improved performance measures, although improvements in preventable hospitalizations have lagged.¹ Evidence to date suggests that patients attributed to an ACO are not going to higher quality SNFs,^2,3 but these effects may be concentrated in hospitals that participate in ACOs and face stronger incentives to alter their discharge patterns compared with non-ACO hospitals. Therefore, we examined whether hospitals participating in Medicare’s Shared Saving Program (MSSP) increased the use of highly rated SNFs or decreased the use of low-rated SNFs hospital-wide after initiation of their ACO contracts compared with non-ACO hospitals.

We used discharge-level data from the 100% MedPAR file for all fee-for-service Medicare beneficiaries discharged from an acute care hospital to an SNF between 2010 and 2013. We measured the SNF quality using Medicare’s Nursing Home Compare star ratings. Our primary outcome was probability of discharge to high-rated (five star) and low-rated (one star) SNFs.

We utilized a difference-in-differences design. Using a linear probability model, we first estimated the change in the probability of discharge to five-star SNFs (compared to all other SNFs) among all beneficiaries discharged from one of the 233 ACO-participating hospitals after the hospital became an ACO provider compared with before and compared withall beneficiaries discharged from one of the 3,081 non-ACO hospitals over the same time period. Individual hospitals were determined to be “ACO-participating” if they were listed on Medicare’s website as being part of an ACO-participating hospital in the MSSP. ACOs joined the MSSP in three waves: April 1, 2012; July 1, 2012; and January 1, 2013, which were also determined based on information on Medicare’s website. We separately estimated the change in probability of discharge to a one-star SNF (compared to all other SNFs) using the same approach. Models were adjusted for beneficiary demographic and clinical characteristics (age, sex, race, dual eligibility, urban ZIP code, diagnosis-related group code, and Elixhauser comorbidities) and market characteristics (the concentration of hospital discharges, SNF discharges, and the number of five-star SNFs, all measured in each hospital referral region).

We examined a total of 12,736,287 discharges, 11.8% from ACO-participating hospitals and 88.2% from non-ACO-participating hospitals. ACO-participating hospitals cared for fewer black patients and fewer patients who were dually enrolled in Medicare and Medicaid (Table 1), but these characteristics did not change differentially between the two groups of hospitals over our study period. ACO-participating hospitals were also more likely to discharge patients to five-star SNFs prior to joining an ACO (in 2010-2011). After joining an ACO, the percentage of hospital discharges going to a 5-star SNF increased by 3.4 percentage points on a base of 15.4% (95% confidence interval [CI] 1.3-5.5, P = .002; Table 2) compared with non-ACO-participating hospitals over the same time period. The differential changes did not extend to SNFs rated as three stars and above (change of 0.5 percentage points, 95% CI, 1.3-2.8, P = .600).

The probability of discharge from an ACO hospital to low-quality (one-star) SNFs did not change significantly from its baseline level of 13.5% after joining an ACO compared with non-ACO-participating hospitals (change of 0.4 percentage points, 95% CI, 0.7-1.5, P = .494).

Our findings indicate that ACO-participating hospitals were more likely to discharge patients to the highest rated SNFs after they began their ACO contract but did not change the likelihood of discharge to lower rated SNFs in comparison with non-ACO hospitals. Previous research has suggested that patients attributed to a Medicare ACO were not more likely to use high-quality SNFs. However, we examined the effect of hospital participation in an ACO, not individual beneficiaries attributed to an ACO. These contrasting results suggest that hospitals could be instituting hospital-wide changes in discharge patterns once they join an ACO and that hospital-led ACOs could be particularly well positioned to manage postdischarge care relative to physician-led ACOs. One potential limitation of this study is that ACO-participating hospitals may differ in unobservable ways from non-ACO-participating hospitals. However, using hospital fixed effects, we mitigated this limitation to some extent by controlling for time-invariant observed and unobserved characteristics. Further work will need to explore the mechanisms of higher PAC quality, including hospital-SNF integration and coordination.

Disclosures

Dr. Werner reports receiving personal fees from CarePort Health. Dr. Bain reports no conflicts. Mr. Yuan reports no conflicts. Dr. Navathe reports receiving personal fees from Navvis and Company, Navigant Inc., Lynx Medical, Indegene Inc., Sutherland Global Services, and Agathos, Inc.; personal fees and equity from NavaHealth; an honorarium from Elsevier Press, serving on the board of Integrated Services, Inc. without compensation, and grants from Hawaii Medical Service Association, Anthem Public Policy Institute, and Oscar Health, none of which are related to this manuscript.

Funding

This research was funded by R01-HS024266 by the Agency for Healthcare Research and Quality. Rachel Werner was supported in part by K24-AG047908 from the National Institute on Aging.

Accountable care organizations (ACOs) create incentives for more efficient healthcare utilization. For patients being discharged from the hospital, this may mean more efficient use of postacute care (PAC), including discharging patients to higher quality skilled nursing facilities (SNFs) in an effort to limit readmissions and other costly complications. Public reporting of nursing home quality has been associated with improved performance measures, although improvements in preventable hospitalizations have lagged.¹ Evidence to date suggests that patients attributed to an ACO are not going to higher quality SNFs,^2,3 but these effects may be concentrated in hospitals that participate in ACOs and face stronger incentives to alter their discharge patterns compared with non-ACO hospitals. Therefore, we examined whether hospitals participating in Medicare’s Shared Saving Program (MSSP) increased the use of highly rated SNFs or decreased the use of low-rated SNFs hospital-wide after initiation of their ACO contracts compared with non-ACO hospitals.

We used discharge-level data from the 100% MedPAR file for all fee-for-service Medicare beneficiaries discharged from an acute care hospital to an SNF between 2010 and 2013. We measured the SNF quality using Medicare’s Nursing Home Compare star ratings. Our primary outcome was probability of discharge to high-rated (five star) and low-rated (one star) SNFs.

We utilized a difference-in-differences design. Using a linear probability model, we first estimated the change in the probability of discharge to five-star SNFs (compared to all other SNFs) among all beneficiaries discharged from one of the 233 ACO-participating hospitals after the hospital became an ACO provider compared with before and compared withall beneficiaries discharged from one of the 3,081 non-ACO hospitals over the same time period. Individual hospitals were determined to be “ACO-participating” if they were listed on Medicare’s website as being part of an ACO-participating hospital in the MSSP. ACOs joined the MSSP in three waves: April 1, 2012; July 1, 2012; and January 1, 2013, which were also determined based on information on Medicare’s website. We separately estimated the change in probability of discharge to a one-star SNF (compared to all other SNFs) using the same approach. Models were adjusted for beneficiary demographic and clinical characteristics (age, sex, race, dual eligibility, urban ZIP code, diagnosis-related group code, and Elixhauser comorbidities) and market characteristics (the concentration of hospital discharges, SNF discharges, and the number of five-star SNFs, all measured in each hospital referral region).

We examined a total of 12,736,287 discharges, 11.8% from ACO-participating hospitals and 88.2% from non-ACO-participating hospitals. ACO-participating hospitals cared for fewer black patients and fewer patients who were dually enrolled in Medicare and Medicaid (Table 1), but these characteristics did not change differentially between the two groups of hospitals over our study period. ACO-participating hospitals were also more likely to discharge patients to five-star SNFs prior to joining an ACO (in 2010-2011). After joining an ACO, the percentage of hospital discharges going to a 5-star SNF increased by 3.4 percentage points on a base of 15.4% (95% confidence interval [CI] 1.3-5.5, P = .002; Table 2) compared with non-ACO-participating hospitals over the same time period. The differential changes did not extend to SNFs rated as three stars and above (change of 0.5 percentage points, 95% CI, 1.3-2.8, P = .600).

The probability of discharge from an ACO hospital to low-quality (one-star) SNFs did not change significantly from its baseline level of 13.5% after joining an ACO compared with non-ACO-participating hospitals (change of 0.4 percentage points, 95% CI, 0.7-1.5, P = .494).

Our findings indicate that ACO-participating hospitals were more likely to discharge patients to the highest rated SNFs after they began their ACO contract but did not change the likelihood of discharge to lower rated SNFs in comparison with non-ACO hospitals. Previous research has suggested that patients attributed to a Medicare ACO were not more likely to use high-quality SNFs. However, we examined the effect of hospital participation in an ACO, not individual beneficiaries attributed to an ACO. These contrasting results suggest that hospitals could be instituting hospital-wide changes in discharge patterns once they join an ACO and that hospital-led ACOs could be particularly well positioned to manage postdischarge care relative to physician-led ACOs. One potential limitation of this study is that ACO-participating hospitals may differ in unobservable ways from non-ACO-participating hospitals. However, using hospital fixed effects, we mitigated this limitation to some extent by controlling for time-invariant observed and unobserved characteristics. Further work will need to explore the mechanisms of higher PAC quality, including hospital-SNF integration and coordination.

Disclosures

Dr. Werner reports receiving personal fees from CarePort Health. Dr. Bain reports no conflicts. Mr. Yuan reports no conflicts. Dr. Navathe reports receiving personal fees from Navvis and Company, Navigant Inc., Lynx Medical, Indegene Inc., Sutherland Global Services, and Agathos, Inc.; personal fees and equity from NavaHealth; an honorarium from Elsevier Press, serving on the board of Integrated Services, Inc. without compensation, and grants from Hawaii Medical Service Association, Anthem Public Policy Institute, and Oscar Health, none of which are related to this manuscript.

Funding

This research was funded by R01-HS024266 by the Agency for Healthcare Research and Quality. Rachel Werner was supported in part by K24-AG047908 from the National Institute on Aging.

Transitions of care, such as hospital discharge, represent a moment of patient vulnerability that can contribute to the occurrence of medication errors and, consequently, hospital readmissions and mortality.¹ Clinical pharmacists have the potential to optimize the pharmacotherapy, patient safety, and process of care during these transitions, reducing negative outcomes.^2,3

Previous studies have shown that pharmacist interventions at hospital discharge, such as medication review, medication reconciliation, and patient counseling, significantly improve medication adherence and reduce adverse drug reactions, hospital readmission rates, and mortality.^3-8 A recent systematic review, including nine clinical trials, showed that clinical pharmacy services performed in an inpatient setting significantly enhanced quality, safety, and efficiency of care when compared with usual care.⁶ Another study referred to pharmacist-led discharge counseling as a cost-effective intervention that may lead to cost savings of 48% in the healthcare setting.⁹ However, as other studies report no significant impact of pharmacist-led medication counseling at discharge on patient outcomes,^9-13 the current benefit or otherwise of such interventions remains uncertain.

Thus, given the inconsistent conclusions about the real effect of pharmacist interventions and the scarcity of systematic reviews regarding patient counseling, we aimed to synthesize the available evidence on the effect of pharmacist-led discharge counseling on healthcare services utilization (ie, hospital readmission and emergency department visit rates) through a systematic review and meta-analysis.

This systematic review was conducted following the PRISMA statement and Cochrane recommendations^14,15 and was registered in PROSPERO (registration no. CRD42017068444). Screening of titles and abstracts, full-text appraisal, data extraction, and study quality assessment were performed by two reviewers independently, with discrepancies discussed with a third reviewer.

Search and Eligibility Criteria

Systematic searches were conducted in PubMed, Scopus, and DOAJ (Directory of Open Access Journals), without limits for timeframe or language (last updated on November 20, 2018). We performed an additional manual search in the reference lists of the included studies. The following descriptors combined with the Boolean operators “AND” and “OR” were used: “discharge,” “counseling,” and “pharmacist.” The full search strategies are outlined in the Supplemental Material.

We included randomized, controlled trials (RCTs) that compared the intervention of pharmacist-led discharge medication counseling versus usual care. Usual care was defined as patients who received the usual treatment in regular practice. The outcomes of interest were the numbers of hospital readmissions and emergency department visits. Patients of any clinical condition, gender, or age were included. The following exclusion criteria were applied: (1) discharge counseling performed by another healthcare professional or a multidisciplinary team, (2) comparison between pharmacist-led discharge counseling and another healthcare professional’s intervention, (3) studies with a control group also receiving discharge counseling by a pharmacist, (4) study designs other than RCTs, (5) studies that reported other pharmacist interventions, but not discharge counseling, (6) counseling not performed at discharge, and (7) studies not reporting the outcomes of interest.

Data Extraction and Quality Assessment

We used a standardized form to collect data on the following general characteristics of the studies: baseline data (author names, year of publication, study design, country, and sample size), methodological aspects, and outcomes of interest (ie, number of hospital readmission or emergency department visits). When outcomes were assessed in different time periods, the last period was considered for the overall analysis.

The methodological quality of the included studies was evaluated using the Cochrane Collaboration’s tool for risk of bias assessment that classifies each study as having a low, unclear, or high risk of bias.¹⁴

Data Analysis

Pairwise meta-analyses of the included RCTs were performed using the Comprehensive Meta-Analysis v 2.2 software (Biostat, Englewood, New Jersey). For each meta-analysis, we used the random effects model with the inverse variance method (DerSimonian and Laird) to estimate the pooled risk ratio (RR) with a 95% confidence interval (CI). With this method, a weight is given to each study that is the inverse of the variance of the effect estimate giving larger studies more weight than smaller studies. P values <.05 (two-tailed) were considered indicative of a statistically significant difference between groups.

The between-trial heterogeneity was estimated using the inconsistency relative index I² (I² > 50% indicates high and significant heterogeneity). Tau and Tau² measures were used to estimate the distribution of the true effect sizes and to compute the prediction intervals (PIs).^16-18 The calculation of PI was done in preformatted sheets in Excel considering the number of studies, the mean effect (random effect weights), the upper effect of mean effect, and tau-square in log units (normal approximation).¹⁷ PIs allow more informative inferences in meta-analyses (eg, true treatment effects that can be expected in future settings), especially when there is large variation in the strength of the effect (high heterogeneity between studies). This results in PIs generally having a wider range of expected treatment effects than CIs.¹⁹

We also conducted sensitivity analyses to test the robustness of the results and to evaluate the effect of individual studies on data heterogeneity. The sensitivity analysis consisted of the hypothetical sequential removal of studies from the meta-analysis. In addition, to verify the influence of small-study effects on the results of a meta-analysis with between-trial heterogeneity (I² > 0), we compared the results obtained in the random effect model with those obtained from fixed effects models.

When possible, subgroup analyses were performed considering (1) how discharge counseling was delivered (ie, alone or combined with other interventions) and (2) time of evaluation of the outcomes (weeks, months, or years postdischarge). The visual representation of the estimated treatment effect versus the standard error (funnel plots) was also performed to assess the potential role of publication bias.

A total of 2,656 records were retrieved from the electronic databases and manual searches. During the screening phase, 276 records were considered for full-text analysis, of which 21 were included in the qualitative analysis^20-40 and 18 were suitable for quantitative analyses^{21,22,24-36,38-40} (Figure 1). The references of excluded studies, with the reasons for exclusion, are mentioned in the Supplemental Material.

The baseline characteristics of the included studies are presented in the Table. A total of 7,244 patients were included in this systematic review, most of them being 60 years or older (81%) and presenting chronic conditions (38.1%) such as cardiovascular and respiratory diseases. The majority of studies were performed in Europe (42.85%), followed by those conducted in the United States of America (28.6%). Overall, studies were classified as high risk of bias (57.14%), because most of them presented two or more domains with unclear risk of bias, especially due to the attrition domain (see Supplemental Material for complete analyses). Given the complexity of pharmacist interventions and the impossibility of blinding participants and personnel, the performance domain of the risk of bias tool was not assessed. Only three studies were considered as low risk of bias for all domains.^22,37,40 Analyses on publication bias were performed by visualization of funnel plots and showed overall symmetry in all cases, which demonstrates a relative lack of bias. Few studies contributed to a slight asymmetry in the plots. Additional information is found in the Supplemental Material.

Readmission Rates

A total of 18 studies evaluating the impact of pharmacist-led discharge counseling on hospital readmission were included in the meta-analysis.^{21,22,24-36,38-40} The studies by Al-Hashar et al., Bolas et al., and Schnniper et al. were excluded from statistical analyses due to a lack of sufficient data.^20,23,27 The results revealed statistical differences between the intervention and usual care (RR = 0.864 [95% CI 0.763-0.997], P = .020; Figure 2). However, the heterogeneity among studies was high (I² approximately 50%) and the calculation of PI revealed a wider interval, with the loss of the statistical significance (Tau = 0.151; PI 0.542-1.186). Sensitivity analyses with the hypothetical removal of trials showed few reductions in heterogeneity (I² values ranging from 35.37% to 49.53%) with similar effect size values.

Subgroup analyses considering the time of hospital admission postdischarge (groups for two to three weeks, one month, three months, six months, and one year) did not demonstrate that pharmacist-led counseling reduced the number of hospital readmissions at any time (see Supplemental Material). Again, more than one study contributed to the moderately high heterogeneity in some subgroups (initial I² values of 49.69% [one month], 69.43% [three months], 50.99% [six months], and 65.55% [one year]). The subgroups of two to three weeks and six months included few studies and caution should be used when interpreting such results (small meta-analysis with wide CIs; I² value of 0%). Sensitivity analyses did not modify the original results (I² values ranging from 35.37% to 49.56%).

In the subgroup analyses of how pharmacist interventions were delivered (ie, discharge counseling alone or combined with other interventions), interventions were superior to usual care, but again, few studies were evaluated, and the sensitivity analyses and calculation of PI revealed no true differences between groups. The meta-analysis for discharge counseling alone presented an RR of 0.333 (95% CI 0.129-0.858, P = .023; Supplemental Material), with three studies included (I² = 48.0%, and Tau = 0.582, PI –11.221-11.880).^21,25,35 The meta-analysis of other interventions showed an RR of 0.898 (95% CI 0.813-0.991, P = .033) (I² = 28.9%; PI 0.690-1.099).^{22,24-36,38-40} The detailed results of PIs are reported in the Supplemental Material.

Emergency Department Visit Rates

A total of eight studies evaluating the impact of pharmacist-led discharge counseling on emergency department visits were included in the meta-analysis.^{21,22,24,26,32-34,39} For the study by Farris et al., we used data from the “minimal intervention” branch.²⁶ Although the original results showed differences between intervention and usual care (RR = 0.697 [95% CI 0.535-0.907], P = .007; Figure 3), the meta-analysis presented high heterogeneity with an I² value of 58.86% (Tau = 0.265; PI 0.027-1.367). Sensitivity analyses with the hypothetical removal of studies did not modify the original results (I² values ranging from 26.05% to 64.74%).

Subgroup analyses considering time of evaluation of the outcome were possible for studies of one, three, and six months postdischarge (Supplemental Material). No statistical differences were observed for the subgroup of one month (RR = 0.705 [95% CI 0.449-1.106] with the original I² = 65.5%). Sensitivity analyses showed that the study by Phatak et al. was responsible for the high heterogeneity (results of I² = 38% after removing this trial),³² without significant changes in the effect sizes. The three-month subgroup included only two studies and presented an RR of 0.763 (95% CI 0.599-0.972, P = .028).^21,26 However, sensitivity analysis based on statistical modifications in the model altered the results, and no differences between the intervention and usual care were truly observed (eg, using the inverse variance method, the random model produced an odds ratio of 0.575 [95% CI 0.219-1.512]). Pharmacist-led counseling reduced the number of emergency department visits at six months postdischarge, RR = 0.605 (95% CI 0.459-0.768, P = .001), but only two studies were included in this analysis.^33,39

The present systematic review and meta-analysis showed equivocal results on the effect of pharmacist-led discharge counseling compared with usual care in reducing the number of hospital readmissions and emergency department visits. Although some of the meta-analyses slightly favored pharmacist interventions, the small number of primary studies included and the moderate-to-high heterogeneity among trials prevented drawing further conclusions. Moreover, sensitivity analyses and PIs revealed no true differences between the intervention and usual care.

Pharmacist interventions are generally complex, being constituted by several components,⁴¹ which are frequently poorly described in the literature and generally inconsistently performed.^42-44 These factors can contribute to reduced methodological quality and enhanced heterogeneity, as reported in previous systematic reviews and meta-analyses.^8,42,45-47 Moreover, the characteristics of the included patients (eg, different clinical conditions) and the small sample sizes may have increased heterogeneity among trials in our meta-analyses.

Similar to our results, El Hajj et al. were not able to demonstrate significant differences between usual care and pharmacist interventions in the transition of care (eg, medication reconciliation, medication therapy management, discharge medication counseling, motivational interviewing, and postdischarge face-to-face or telephone follow-up) in reducing rates of hospital readmission, visits to emergency units, and mortality, or in improving medication adherence.¹¹ Another systematic review with a meta-analysis also showed that interventions, including discharge counseling, did not reduce the number of hospital readmissions (RR = 0.97 [95% CI 0.89-1.05], P = .470) and visits to emergency units (RR = 0.70 [95% CI 0.59-0.85] P = .001).⁴⁸ However, both systematic reviews included few RCTs with moderate methodological quality, which may compromise interpretation of the results. In this case, imprecision in estimates and individual study results may be more informative than a meta-analysis.

Ensing et al. highlighted the need for more well-designed RCTs for clinical pharmacy services to provide high-quality information to be included in systematic reviews and meta-analyses.⁴⁹ This may enable the identification of the true effect of pharmacist interventions in patient care.⁴⁰ In our systematic review, the high risk of bias in some included studies was attributed especially to the attrition domain, indicating that the outcomes were poorly evaluated or patient losses and withdrawals were not sufficiently described. In addition, most of the studies had an unclear risk of bias, primarily because of poor descriptions of the blindness of the outcome assessors. These pitfalls highlight the need for more rigorous standards for carrying out and reporting RCTs on pharmacist interventions, which should be strictly required by journal editors and reviewers.⁵⁰Moreover, the standardization of outcomes is also important to allow comparability between studies. Core outcome sets represent agreed sets of outcomes that should be measured and reported by trials in a specific area, as recommend by the COMET Initiative (Core Outcome Measures in Effectiveness Trials).⁵¹ Pharmacy practice studies have started defining core outcome sets to be used in future trials,^52-54 as recently happened for pharmacist-led discharge counseling.⁵⁵ It is important to keep in mind the different implications resulting from the use of endpoint outcomes, surrogate outcomes, or process indicators. Although the latter are easily measured but also easily influenced by interventions, endpoint outcomes represent the real impact of the interventions that should be used in economic evaluations.⁵⁶ Surrogate outcomes are frequently used as a proxy of endpoint outcomes, but precaution is needed when inferring conclusions.⁵⁷ In our study, we preferred using healthcare services utilization as a measure of intervention success. However, these outcomes could also be affected by other factors not related to medication safety. The use of properly designed RCTs and their synthesis in robust meta-analyses should minimize potential interpretation biases.

Our findings also show the need to better define clinical pharmacy services. A better description of interventions is important to not only allow evidence gathering but also enable the proper replication of complex interventions in practice and to ground further analyses on the economic impact of pharmacist interventions.

Our study has some limitations. Although subgroup and sensitivity analyses were performed, we were not able to reduce the heterogeneity and effect size intervals of some meta-analyses. Caution should be used when interpreting the results from the subgroup meta-analysis, including small numbers of studies (n = 2-4). The absent or minor effects of pharmacist-led interventions on healthcare services utilization found in our study may be due to a real lack of measurable effect of the intervention itself or due to the limited evidence available in the literature. This is related to the small number of primary studies, poor reporting practices, and high heterogeneity between trials. In addition, another limitation that affects our study is the poor measurement of intervention fidelity in primary studies, which precludes an in-depth analysis of the effect of the different intervention components. A better report of intervention fidelity would allow a different sensitive analysis that could differentiate the most successful interventions.

Similar to what happens with other complex interventions by pharmacists, a detailed description of the procedure, together with reporting on a core outcome set, is needed to enhance reproducibility. Future RCTs of clinical pharmacy services that follow standard protocols such as DEPICT⁵⁸ and CONSORT⁵⁹ and report in detail how the study and the interventions were performed will contribute to more robust evidence generation.

Transitions of care, such as hospital discharge, represent a moment of patient vulnerability that can contribute to the occurrence of medication errors and, consequently, hospital readmissions and mortality.¹ Clinical pharmacists have the potential to optimize the pharmacotherapy, patient safety, and process of care during these transitions, reducing negative outcomes.^2,3

Previous studies have shown that pharmacist interventions at hospital discharge, such as medication review, medication reconciliation, and patient counseling, significantly improve medication adherence and reduce adverse drug reactions, hospital readmission rates, and mortality.^3-8 A recent systematic review, including nine clinical trials, showed that clinical pharmacy services performed in an inpatient setting significantly enhanced quality, safety, and efficiency of care when compared with usual care.⁶ Another study referred to pharmacist-led discharge counseling as a cost-effective intervention that may lead to cost savings of 48% in the healthcare setting.⁹ However, as other studies report no significant impact of pharmacist-led medication counseling at discharge on patient outcomes,^9-13 the current benefit or otherwise of such interventions remains uncertain.

Thus, given the inconsistent conclusions about the real effect of pharmacist interventions and the scarcity of systematic reviews regarding patient counseling, we aimed to synthesize the available evidence on the effect of pharmacist-led discharge counseling on healthcare services utilization (ie, hospital readmission and emergency department visit rates) through a systematic review and meta-analysis.

This systematic review was conducted following the PRISMA statement and Cochrane recommendations^14,15 and was registered in PROSPERO (registration no. CRD42017068444). Screening of titles and abstracts, full-text appraisal, data extraction, and study quality assessment were performed by two reviewers independently, with discrepancies discussed with a third reviewer.

Search and Eligibility Criteria

Systematic searches were conducted in PubMed, Scopus, and DOAJ (Directory of Open Access Journals), without limits for timeframe or language (last updated on November 20, 2018). We performed an additional manual search in the reference lists of the included studies. The following descriptors combined with the Boolean operators “AND” and “OR” were used: “discharge,” “counseling,” and “pharmacist.” The full search strategies are outlined in the Supplemental Material.

We included randomized, controlled trials (RCTs) that compared the intervention of pharmacist-led discharge medication counseling versus usual care. Usual care was defined as patients who received the usual treatment in regular practice. The outcomes of interest were the numbers of hospital readmissions and emergency department visits. Patients of any clinical condition, gender, or age were included. The following exclusion criteria were applied: (1) discharge counseling performed by another healthcare professional or a multidisciplinary team, (2) comparison between pharmacist-led discharge counseling and another healthcare professional’s intervention, (3) studies with a control group also receiving discharge counseling by a pharmacist, (4) study designs other than RCTs, (5) studies that reported other pharmacist interventions, but not discharge counseling, (6) counseling not performed at discharge, and (7) studies not reporting the outcomes of interest.

Data Extraction and Quality Assessment

We used a standardized form to collect data on the following general characteristics of the studies: baseline data (author names, year of publication, study design, country, and sample size), methodological aspects, and outcomes of interest (ie, number of hospital readmission or emergency department visits). When outcomes were assessed in different time periods, the last period was considered for the overall analysis.

The methodological quality of the included studies was evaluated using the Cochrane Collaboration’s tool for risk of bias assessment that classifies each study as having a low, unclear, or high risk of bias.¹⁴

Data Analysis

Pairwise meta-analyses of the included RCTs were performed using the Comprehensive Meta-Analysis v 2.2 software (Biostat, Englewood, New Jersey). For each meta-analysis, we used the random effects model with the inverse variance method (DerSimonian and Laird) to estimate the pooled risk ratio (RR) with a 95% confidence interval (CI). With this method, a weight is given to each study that is the inverse of the variance of the effect estimate giving larger studies more weight than smaller studies. P values <.05 (two-tailed) were considered indicative of a statistically significant difference between groups.

The between-trial heterogeneity was estimated using the inconsistency relative index I² (I² > 50% indicates high and significant heterogeneity). Tau and Tau² measures were used to estimate the distribution of the true effect sizes and to compute the prediction intervals (PIs).^16-18 The calculation of PI was done in preformatted sheets in Excel considering the number of studies, the mean effect (random effect weights), the upper effect of mean effect, and tau-square in log units (normal approximation).¹⁷ PIs allow more informative inferences in meta-analyses (eg, true treatment effects that can be expected in future settings), especially when there is large variation in the strength of the effect (high heterogeneity between studies). This results in PIs generally having a wider range of expected treatment effects than CIs.¹⁹

We also conducted sensitivity analyses to test the robustness of the results and to evaluate the effect of individual studies on data heterogeneity. The sensitivity analysis consisted of the hypothetical sequential removal of studies from the meta-analysis. In addition, to verify the influence of small-study effects on the results of a meta-analysis with between-trial heterogeneity (I² > 0), we compared the results obtained in the random effect model with those obtained from fixed effects models.

When possible, subgroup analyses were performed considering (1) how discharge counseling was delivered (ie, alone or combined with other interventions) and (2) time of evaluation of the outcomes (weeks, months, or years postdischarge). The visual representation of the estimated treatment effect versus the standard error (funnel plots) was also performed to assess the potential role of publication bias.

A total of 2,656 records were retrieved from the electronic databases and manual searches. During the screening phase, 276 records were considered for full-text analysis, of which 21 were included in the qualitative analysis^20-40 and 18 were suitable for quantitative analyses^{21,22,24-36,38-40} (Figure 1). The references of excluded studies, with the reasons for exclusion, are mentioned in the Supplemental Material.

The baseline characteristics of the included studies are presented in the Table. A total of 7,244 patients were included in this systematic review, most of them being 60 years or older (81%) and presenting chronic conditions (38.1%) such as cardiovascular and respiratory diseases. The majority of studies were performed in Europe (42.85%), followed by those conducted in the United States of America (28.6%). Overall, studies were classified as high risk of bias (57.14%), because most of them presented two or more domains with unclear risk of bias, especially due to the attrition domain (see Supplemental Material for complete analyses). Given the complexity of pharmacist interventions and the impossibility of blinding participants and personnel, the performance domain of the risk of bias tool was not assessed. Only three studies were considered as low risk of bias for all domains.^22,37,40 Analyses on publication bias were performed by visualization of funnel plots and showed overall symmetry in all cases, which demonstrates a relative lack of bias. Few studies contributed to a slight asymmetry in the plots. Additional information is found in the Supplemental Material.

Readmission Rates

A total of 18 studies evaluating the impact of pharmacist-led discharge counseling on hospital readmission were included in the meta-analysis.^{21,22,24-36,38-40} The studies by Al-Hashar et al., Bolas et al., and Schnniper et al. were excluded from statistical analyses due to a lack of sufficient data.^20,23,27 The results revealed statistical differences between the intervention and usual care (RR = 0.864 [95% CI 0.763-0.997], P = .020; Figure 2). However, the heterogeneity among studies was high (I² approximately 50%) and the calculation of PI revealed a wider interval, with the loss of the statistical significance (Tau = 0.151; PI 0.542-1.186). Sensitivity analyses with the hypothetical removal of trials showed few reductions in heterogeneity (I² values ranging from 35.37% to 49.53%) with similar effect size values.

Subgroup analyses considering the time of hospital admission postdischarge (groups for two to three weeks, one month, three months, six months, and one year) did not demonstrate that pharmacist-led counseling reduced the number of hospital readmissions at any time (see Supplemental Material). Again, more than one study contributed to the moderately high heterogeneity in some subgroups (initial I² values of 49.69% [one month], 69.43% [three months], 50.99% [six months], and 65.55% [one year]). The subgroups of two to three weeks and six months included few studies and caution should be used when interpreting such results (small meta-analysis with wide CIs; I² value of 0%). Sensitivity analyses did not modify the original results (I² values ranging from 35.37% to 49.56%).

In the subgroup analyses of how pharmacist interventions were delivered (ie, discharge counseling alone or combined with other interventions), interventions were superior to usual care, but again, few studies were evaluated, and the sensitivity analyses and calculation of PI revealed no true differences between groups. The meta-analysis for discharge counseling alone presented an RR of 0.333 (95% CI 0.129-0.858, P = .023; Supplemental Material), with three studies included (I² = 48.0%, and Tau = 0.582, PI –11.221-11.880).^21,25,35 The meta-analysis of other interventions showed an RR of 0.898 (95% CI 0.813-0.991, P = .033) (I² = 28.9%; PI 0.690-1.099).^{22,24-36,38-40} The detailed results of PIs are reported in the Supplemental Material.

Emergency Department Visit Rates

A total of eight studies evaluating the impact of pharmacist-led discharge counseling on emergency department visits were included in the meta-analysis.^{21,22,24,26,32-34,39} For the study by Farris et al., we used data from the “minimal intervention” branch.²⁶ Although the original results showed differences between intervention and usual care (RR = 0.697 [95% CI 0.535-0.907], P = .007; Figure 3), the meta-analysis presented high heterogeneity with an I² value of 58.86% (Tau = 0.265; PI 0.027-1.367). Sensitivity analyses with the hypothetical removal of studies did not modify the original results (I² values ranging from 26.05% to 64.74%).

Subgroup analyses considering time of evaluation of the outcome were possible for studies of one, three, and six months postdischarge (Supplemental Material). No statistical differences were observed for the subgroup of one month (RR = 0.705 [95% CI 0.449-1.106] with the original I² = 65.5%). Sensitivity analyses showed that the study by Phatak et al. was responsible for the high heterogeneity (results of I² = 38% after removing this trial),³² without significant changes in the effect sizes. The three-month subgroup included only two studies and presented an RR of 0.763 (95% CI 0.599-0.972, P = .028).^21,26 However, sensitivity analysis based on statistical modifications in the model altered the results, and no differences between the intervention and usual care were truly observed (eg, using the inverse variance method, the random model produced an odds ratio of 0.575 [95% CI 0.219-1.512]). Pharmacist-led counseling reduced the number of emergency department visits at six months postdischarge, RR = 0.605 (95% CI 0.459-0.768, P = .001), but only two studies were included in this analysis.^33,39

The present systematic review and meta-analysis showed equivocal results on the effect of pharmacist-led discharge counseling compared with usual care in reducing the number of hospital readmissions and emergency department visits. Although some of the meta-analyses slightly favored pharmacist interventions, the small number of primary studies included and the moderate-to-high heterogeneity among trials prevented drawing further conclusions. Moreover, sensitivity analyses and PIs revealed no true differences between the intervention and usual care.

Pharmacist interventions are generally complex, being constituted by several components,⁴¹ which are frequently poorly described in the literature and generally inconsistently performed.^42-44 These factors can contribute to reduced methodological quality and enhanced heterogeneity, as reported in previous systematic reviews and meta-analyses.^8,42,45-47 Moreover, the characteristics of the included patients (eg, different clinical conditions) and the small sample sizes may have increased heterogeneity among trials in our meta-analyses.

Similar to our results, El Hajj et al. were not able to demonstrate significant differences between usual care and pharmacist interventions in the transition of care (eg, medication reconciliation, medication therapy management, discharge medication counseling, motivational interviewing, and postdischarge face-to-face or telephone follow-up) in reducing rates of hospital readmission, visits to emergency units, and mortality, or in improving medication adherence.¹¹ Another systematic review with a meta-analysis also showed that interventions, including discharge counseling, did not reduce the number of hospital readmissions (RR = 0.97 [95% CI 0.89-1.05], P = .470) and visits to emergency units (RR = 0.70 [95% CI 0.59-0.85] P = .001).⁴⁸ However, both systematic reviews included few RCTs with moderate methodological quality, which may compromise interpretation of the results. In this case, imprecision in estimates and individual study results may be more informative than a meta-analysis.

Ensing et al. highlighted the need for more well-designed RCTs for clinical pharmacy services to provide high-quality information to be included in systematic reviews and meta-analyses.⁴⁹ This may enable the identification of the true effect of pharmacist interventions in patient care.⁴⁰ In our systematic review, the high risk of bias in some included studies was attributed especially to the attrition domain, indicating that the outcomes were poorly evaluated or patient losses and withdrawals were not sufficiently described. In addition, most of the studies had an unclear risk of bias, primarily because of poor descriptions of the blindness of the outcome assessors. These pitfalls highlight the need for more rigorous standards for carrying out and reporting RCTs on pharmacist interventions, which should be strictly required by journal editors and reviewers.⁵⁰Moreover, the standardization of outcomes is also important to allow comparability between studies. Core outcome sets represent agreed sets of outcomes that should be measured and reported by trials in a specific area, as recommend by the COMET Initiative (Core Outcome Measures in Effectiveness Trials).⁵¹ Pharmacy practice studies have started defining core outcome sets to be used in future trials,^52-54 as recently happened for pharmacist-led discharge counseling.⁵⁵ It is important to keep in mind the different implications resulting from the use of endpoint outcomes, surrogate outcomes, or process indicators. Although the latter are easily measured but also easily influenced by interventions, endpoint outcomes represent the real impact of the interventions that should be used in economic evaluations.⁵⁶ Surrogate outcomes are frequently used as a proxy of endpoint outcomes, but precaution is needed when inferring conclusions.⁵⁷ In our study, we preferred using healthcare services utilization as a measure of intervention success. However, these outcomes could also be affected by other factors not related to medication safety. The use of properly designed RCTs and their synthesis in robust meta-analyses should minimize potential interpretation biases.

Our findings also show the need to better define clinical pharmacy services. A better description of interventions is important to not only allow evidence gathering but also enable the proper replication of complex interventions in practice and to ground further analyses on the economic impact of pharmacist interventions.

Our study has some limitations. Although subgroup and sensitivity analyses were performed, we were not able to reduce the heterogeneity and effect size intervals of some meta-analyses. Caution should be used when interpreting the results from the subgroup meta-analysis, including small numbers of studies (n = 2-4). The absent or minor effects of pharmacist-led interventions on healthcare services utilization found in our study may be due to a real lack of measurable effect of the intervention itself or due to the limited evidence available in the literature. This is related to the small number of primary studies, poor reporting practices, and high heterogeneity between trials. In addition, another limitation that affects our study is the poor measurement of intervention fidelity in primary studies, which precludes an in-depth analysis of the effect of the different intervention components. A better report of intervention fidelity would allow a different sensitive analysis that could differentiate the most successful interventions.

Similar to what happens with other complex interventions by pharmacists, a detailed description of the procedure, together with reporting on a core outcome set, is needed to enhance reproducibility. Future RCTs of clinical pharmacy services that follow standard protocols such as DEPICT⁵⁸ and CONSORT⁵⁹ and report in detail how the study and the interventions were performed will contribute to more robust evidence generation.

Transitions of care, such as hospital discharge, represent a moment of patient vulnerability that can contribute to the occurrence of medication errors and, consequently, hospital readmissions and mortality.¹ Clinical pharmacists have the potential to optimize the pharmacotherapy, patient safety, and process of care during these transitions, reducing negative outcomes.^2,3

Previous studies have shown that pharmacist interventions at hospital discharge, such as medication review, medication reconciliation, and patient counseling, significantly improve medication adherence and reduce adverse drug reactions, hospital readmission rates, and mortality.^3-8 A recent systematic review, including nine clinical trials, showed that clinical pharmacy services performed in an inpatient setting significantly enhanced quality, safety, and efficiency of care when compared with usual care.⁶ Another study referred to pharmacist-led discharge counseling as a cost-effective intervention that may lead to cost savings of 48% in the healthcare setting.⁹ However, as other studies report no significant impact of pharmacist-led medication counseling at discharge on patient outcomes,^9-13 the current benefit or otherwise of such interventions remains uncertain.

Thus, given the inconsistent conclusions about the real effect of pharmacist interventions and the scarcity of systematic reviews regarding patient counseling, we aimed to synthesize the available evidence on the effect of pharmacist-led discharge counseling on healthcare services utilization (ie, hospital readmission and emergency department visit rates) through a systematic review and meta-analysis.

This systematic review was conducted following the PRISMA statement and Cochrane recommendations^14,15 and was registered in PROSPERO (registration no. CRD42017068444). Screening of titles and abstracts, full-text appraisal, data extraction, and study quality assessment were performed by two reviewers independently, with discrepancies discussed with a third reviewer.

Search and Eligibility Criteria

Systematic searches were conducted in PubMed, Scopus, and DOAJ (Directory of Open Access Journals), without limits for timeframe or language (last updated on November 20, 2018). We performed an additional manual search in the reference lists of the included studies. The following descriptors combined with the Boolean operators “AND” and “OR” were used: “discharge,” “counseling,” and “pharmacist.” The full search strategies are outlined in the Supplemental Material.

We included randomized, controlled trials (RCTs) that compared the intervention of pharmacist-led discharge medication counseling versus usual care. Usual care was defined as patients who received the usual treatment in regular practice. The outcomes of interest were the numbers of hospital readmissions and emergency department visits. Patients of any clinical condition, gender, or age were included. The following exclusion criteria were applied: (1) discharge counseling performed by another healthcare professional or a multidisciplinary team, (2) comparison between pharmacist-led discharge counseling and another healthcare professional’s intervention, (3) studies with a control group also receiving discharge counseling by a pharmacist, (4) study designs other than RCTs, (5) studies that reported other pharmacist interventions, but not discharge counseling, (6) counseling not performed at discharge, and (7) studies not reporting the outcomes of interest.

Data Extraction and Quality Assessment

We used a standardized form to collect data on the following general characteristics of the studies: baseline data (author names, year of publication, study design, country, and sample size), methodological aspects, and outcomes of interest (ie, number of hospital readmission or emergency department visits). When outcomes were assessed in different time periods, the last period was considered for the overall analysis.

The methodological quality of the included studies was evaluated using the Cochrane Collaboration’s tool for risk of bias assessment that classifies each study as having a low, unclear, or high risk of bias.¹⁴

Data Analysis

Pairwise meta-analyses of the included RCTs were performed using the Comprehensive Meta-Analysis v 2.2 software (Biostat, Englewood, New Jersey). For each meta-analysis, we used the random effects model with the inverse variance method (DerSimonian and Laird) to estimate the pooled risk ratio (RR) with a 95% confidence interval (CI). With this method, a weight is given to each study that is the inverse of the variance of the effect estimate giving larger studies more weight than smaller studies. P values <.05 (two-tailed) were considered indicative of a statistically significant difference between groups.

The between-trial heterogeneity was estimated using the inconsistency relative index I² (I² > 50% indicates high and significant heterogeneity). Tau and Tau² measures were used to estimate the distribution of the true effect sizes and to compute the prediction intervals (PIs).^16-18 The calculation of PI was done in preformatted sheets in Excel considering the number of studies, the mean effect (random effect weights), the upper effect of mean effect, and tau-square in log units (normal approximation).¹⁷ PIs allow more informative inferences in meta-analyses (eg, true treatment effects that can be expected in future settings), especially when there is large variation in the strength of the effect (high heterogeneity between studies). This results in PIs generally having a wider range of expected treatment effects than CIs.¹⁹

We also conducted sensitivity analyses to test the robustness of the results and to evaluate the effect of individual studies on data heterogeneity. The sensitivity analysis consisted of the hypothetical sequential removal of studies from the meta-analysis. In addition, to verify the influence of small-study effects on the results of a meta-analysis with between-trial heterogeneity (I² > 0), we compared the results obtained in the random effect model with those obtained from fixed effects models.

When possible, subgroup analyses were performed considering (1) how discharge counseling was delivered (ie, alone or combined with other interventions) and (2) time of evaluation of the outcomes (weeks, months, or years postdischarge). The visual representation of the estimated treatment effect versus the standard error (funnel plots) was also performed to assess the potential role of publication bias.

A total of 2,656 records were retrieved from the electronic databases and manual searches. During the screening phase, 276 records were considered for full-text analysis, of which 21 were included in the qualitative analysis^20-40 and 18 were suitable for quantitative analyses^{21,22,24-36,38-40} (Figure 1). The references of excluded studies, with the reasons for exclusion, are mentioned in the Supplemental Material.

The baseline characteristics of the included studies are presented in the Table. A total of 7,244 patients were included in this systematic review, most of them being 60 years or older (81%) and presenting chronic conditions (38.1%) such as cardiovascular and respiratory diseases. The majority of studies were performed in Europe (42.85%), followed by those conducted in the United States of America (28.6%). Overall, studies were classified as high risk of bias (57.14%), because most of them presented two or more domains with unclear risk of bias, especially due to the attrition domain (see Supplemental Material for complete analyses). Given the complexity of pharmacist interventions and the impossibility of blinding participants and personnel, the performance domain of the risk of bias tool was not assessed. Only three studies were considered as low risk of bias for all domains.^22,37,40 Analyses on publication bias were performed by visualization of funnel plots and showed overall symmetry in all cases, which demonstrates a relative lack of bias. Few studies contributed to a slight asymmetry in the plots. Additional information is found in the Supplemental Material.

Readmission Rates

A total of 18 studies evaluating the impact of pharmacist-led discharge counseling on hospital readmission were included in the meta-analysis.^{21,22,24-36,38-40} The studies by Al-Hashar et al., Bolas et al., and Schnniper et al. were excluded from statistical analyses due to a lack of sufficient data.^20,23,27 The results revealed statistical differences between the intervention and usual care (RR = 0.864 [95% CI 0.763-0.997], P = .020; Figure 2). However, the heterogeneity among studies was high (I² approximately 50%) and the calculation of PI revealed a wider interval, with the loss of the statistical significance (Tau = 0.151; PI 0.542-1.186). Sensitivity analyses with the hypothetical removal of trials showed few reductions in heterogeneity (I² values ranging from 35.37% to 49.53%) with similar effect size values.

Subgroup analyses considering the time of hospital admission postdischarge (groups for two to three weeks, one month, three months, six months, and one year) did not demonstrate that pharmacist-led counseling reduced the number of hospital readmissions at any time (see Supplemental Material). Again, more than one study contributed to the moderately high heterogeneity in some subgroups (initial I² values of 49.69% [one month], 69.43% [three months], 50.99% [six months], and 65.55% [one year]). The subgroups of two to three weeks and six months included few studies and caution should be used when interpreting such results (small meta-analysis with wide CIs; I² value of 0%). Sensitivity analyses did not modify the original results (I² values ranging from 35.37% to 49.56%).

In the subgroup analyses of how pharmacist interventions were delivered (ie, discharge counseling alone or combined with other interventions), interventions were superior to usual care, but again, few studies were evaluated, and the sensitivity analyses and calculation of PI revealed no true differences between groups. The meta-analysis for discharge counseling alone presented an RR of 0.333 (95% CI 0.129-0.858, P = .023; Supplemental Material), with three studies included (I² = 48.0%, and Tau = 0.582, PI –11.221-11.880).^21,25,35 The meta-analysis of other interventions showed an RR of 0.898 (95% CI 0.813-0.991, P = .033) (I² = 28.9%; PI 0.690-1.099).^{22,24-36,38-40} The detailed results of PIs are reported in the Supplemental Material.

Emergency Department Visit Rates

A total of eight studies evaluating the impact of pharmacist-led discharge counseling on emergency department visits were included in the meta-analysis.^{21,22,24,26,32-34,39} For the study by Farris et al., we used data from the “minimal intervention” branch.²⁶ Although the original results showed differences between intervention and usual care (RR = 0.697 [95% CI 0.535-0.907], P = .007; Figure 3), the meta-analysis presented high heterogeneity with an I² value of 58.86% (Tau = 0.265; PI 0.027-1.367). Sensitivity analyses with the hypothetical removal of studies did not modify the original results (I² values ranging from 26.05% to 64.74%).

Subgroup analyses considering time of evaluation of the outcome were possible for studies of one, three, and six months postdischarge (Supplemental Material). No statistical differences were observed for the subgroup of one month (RR = 0.705 [95% CI 0.449-1.106] with the original I² = 65.5%). Sensitivity analyses showed that the study by Phatak et al. was responsible for the high heterogeneity (results of I² = 38% after removing this trial),³² without significant changes in the effect sizes. The three-month subgroup included only two studies and presented an RR of 0.763 (95% CI 0.599-0.972, P = .028).^21,26 However, sensitivity analysis based on statistical modifications in the model altered the results, and no differences between the intervention and usual care were truly observed (eg, using the inverse variance method, the random model produced an odds ratio of 0.575 [95% CI 0.219-1.512]). Pharmacist-led counseling reduced the number of emergency department visits at six months postdischarge, RR = 0.605 (95% CI 0.459-0.768, P = .001), but only two studies were included in this analysis.^33,39

The present systematic review and meta-analysis showed equivocal results on the effect of pharmacist-led discharge counseling compared with usual care in reducing the number of hospital readmissions and emergency department visits. Although some of the meta-analyses slightly favored pharmacist interventions, the small number of primary studies included and the moderate-to-high heterogeneity among trials prevented drawing further conclusions. Moreover, sensitivity analyses and PIs revealed no true differences between the intervention and usual care.

Pharmacist interventions are generally complex, being constituted by several components,⁴¹ which are frequently poorly described in the literature and generally inconsistently performed.^42-44 These factors can contribute to reduced methodological quality and enhanced heterogeneity, as reported in previous systematic reviews and meta-analyses.^8,42,45-47 Moreover, the characteristics of the included patients (eg, different clinical conditions) and the small sample sizes may have increased heterogeneity among trials in our meta-analyses.

Similar to our results, El Hajj et al. were not able to demonstrate significant differences between usual care and pharmacist interventions in the transition of care (eg, medication reconciliation, medication therapy management, discharge medication counseling, motivational interviewing, and postdischarge face-to-face or telephone follow-up) in reducing rates of hospital readmission, visits to emergency units, and mortality, or in improving medication adherence.¹¹ Another systematic review with a meta-analysis also showed that interventions, including discharge counseling, did not reduce the number of hospital readmissions (RR = 0.97 [95% CI 0.89-1.05], P = .470) and visits to emergency units (RR = 0.70 [95% CI 0.59-0.85] P = .001).⁴⁸ However, both systematic reviews included few RCTs with moderate methodological quality, which may compromise interpretation of the results. In this case, imprecision in estimates and individual study results may be more informative than a meta-analysis.

Ensing et al. highlighted the need for more well-designed RCTs for clinical pharmacy services to provide high-quality information to be included in systematic reviews and meta-analyses.⁴⁹ This may enable the identification of the true effect of pharmacist interventions in patient care.⁴⁰ In our systematic review, the high risk of bias in some included studies was attributed especially to the attrition domain, indicating that the outcomes were poorly evaluated or patient losses and withdrawals were not sufficiently described. In addition, most of the studies had an unclear risk of bias, primarily because of poor descriptions of the blindness of the outcome assessors. These pitfalls highlight the need for more rigorous standards for carrying out and reporting RCTs on pharmacist interventions, which should be strictly required by journal editors and reviewers.⁵⁰Moreover, the standardization of outcomes is also important to allow comparability between studies. Core outcome sets represent agreed sets of outcomes that should be measured and reported by trials in a specific area, as recommend by the COMET Initiative (Core Outcome Measures in Effectiveness Trials).⁵¹ Pharmacy practice studies have started defining core outcome sets to be used in future trials,^52-54 as recently happened for pharmacist-led discharge counseling.⁵⁵ It is important to keep in mind the different implications resulting from the use of endpoint outcomes, surrogate outcomes, or process indicators. Although the latter are easily measured but also easily influenced by interventions, endpoint outcomes represent the real impact of the interventions that should be used in economic evaluations.⁵⁶ Surrogate outcomes are frequently used as a proxy of endpoint outcomes, but precaution is needed when inferring conclusions.⁵⁷ In our study, we preferred using healthcare services utilization as a measure of intervention success. However, these outcomes could also be affected by other factors not related to medication safety. The use of properly designed RCTs and their synthesis in robust meta-analyses should minimize potential interpretation biases.

Our findings also show the need to better define clinical pharmacy services. A better description of interventions is important to not only allow evidence gathering but also enable the proper replication of complex interventions in practice and to ground further analyses on the economic impact of pharmacist interventions.

Our study has some limitations. Although subgroup and sensitivity analyses were performed, we were not able to reduce the heterogeneity and effect size intervals of some meta-analyses. Caution should be used when interpreting the results from the subgroup meta-analysis, including small numbers of studies (n = 2-4). The absent or minor effects of pharmacist-led interventions on healthcare services utilization found in our study may be due to a real lack of measurable effect of the intervention itself or due to the limited evidence available in the literature. This is related to the small number of primary studies, poor reporting practices, and high heterogeneity between trials. In addition, another limitation that affects our study is the poor measurement of intervention fidelity in primary studies, which precludes an in-depth analysis of the effect of the different intervention components. A better report of intervention fidelity would allow a different sensitive analysis that could differentiate the most successful interventions.

Similar to what happens with other complex interventions by pharmacists, a detailed description of the procedure, together with reporting on a core outcome set, is needed to enhance reproducibility. Future RCTs of clinical pharmacy services that follow standard protocols such as DEPICT⁵⁸ and CONSORT⁵⁹ and report in detail how the study and the interventions were performed will contribute to more robust evidence generation.

Ms. P., an 86-year-old woman with a history of hypertension, hyperlipidemia, coronary artery disease, and transient ischemic attack, presents to the emergency department with a three-day history of cough, fever, purulent sputum, fatigue, and dyspnea on exertion. Her vital signs are notable for a fever of 39.0°C, blood pressure 136/92, pulse 102, respiratory rate 30, and room air oxygen saturation of 91%. She looks ill. She has a white blood cell count of 16,000, lactate 1.9, and a right lower lobe infiltrate on imaging. The emergency department attending physician presents the case to you for admission, and you accept the patient into your inpatient hospitalist service.

Now, let’s imagine a different future in which you are the attending hospitalist on your institution’s Hospital at Home (HaH) service, where you will provide hospital-level care to Ms. P. in the comfort of her own home. Hospitalists should prepare for this paradigm shift.

HaH provides hospital-level care in a patient’s home, for those with qualifying acute illnesses and appropriate degrees of acuity, as a substitute for traditional inpatient care.¹ This is achieved by bringing the critical elements of hospital care to the home—physician and nursing care, intravenous medications and fluids, oxygen and respiratory therapies, basic radiography and ultrasound, durable medical equipment, skilled therapies, and more.²

All hospitalists have cared for patients like Ms. P., and she and many patients like her will have a straightforward hospital trajectory: initial evaluation in the emergency department, inpatient care provided by a hospitalist inpatient service, a few days of intravenous antibiotics and other hospital services, and finally, discharge to home.

However, not all patients will experience a smooth, or safe, hospital course. Studies that launched the hospital safety movement also provide the rationale for HaH, namely, that hospitals are often dangerous environments for patients.³

A complementary approach to improving outcomes for patients at high risk of iatrogenic illness such as functional decline, falls, delirium, adverse drug events, and hospital-associated disability syndrome,^4-6 is to care for patients outside the traditional inpatient hospital environment. Over the past 20 years, many studies—including dozens of randomized controlled trials and several meta-analyses—have shown better outcomes for patients cared for in HaH: decreased length of stay, decreased incidence of adverse events (including substantially lower six-month mortality), better patient and caregiver care experiences, lower caregiver stress, and lower costs.^7-9A recent Center for Medicare and Medicaid Innovation (CMMI) Demonstration conducted at the Mount Sinai Health System found similar results.¹⁰

Interest in HaH has increased markedly over the past few years with increased penetration of Medicare and Medicaid managed care, the development and spread of accountable care organizations (ACOs), and a shift in focus among some health systems towards value-based care, population health, and community-based care. Recently, commercial entities have entered the HaH space and have raised substantial capital to fund development. Despite this growing interest in HaH and substantial evidence of its effectiveness, HaH has not been widely implemented or scaled in the United States.

Widespread dissemination and implementation of HaH has been hampered by several barriers. First, despite growing interest in HaH, the culture of healthcare and health system leadership, for the most part, remains focused on facility-based care.¹¹

Second, while HaH makes financial sense in the managed care arena, given the strong evidence for high-quality, lower-cost care, there is currently no standard payment mechanism for HaH in fee-for-service Medicare or in the commercial insurance space. However, there are indications that this may soon change. In the fall of 2017, a proposal for a bundled payment mechanism for acute HaH care plus 30 days of postacute care was unanimously approved by an Advisory Committee to the Secretary of the Department of Health and Human Services (HHS).^12,13 The HHS Secretary recently noted that “the Department of Health and Human Services is keenly interested in ideas for home-based, hospital-level care, and agrees … that this proposal holds promise for testing.”¹⁴

Third is the need to create the logistics and supply chain to support HaH. There currently exists a well-established supply chain for providing hospital care. A hospitalist orders a dose of intravenous antibiotic or oxygen, and it is supplied in a timely manner. Similarly, the postacute sector of healthcare has a robust supply chain, though it operates on a somewhat different clock from the acute care setting. However, there is currently no easily replicable supply chain to meet the needs of providing acute care in the home. Each HaH has had to create its own system of logistics with the existing healthcare assets in its local environment. Developing this capacity at scale will require significant capital investment.

There are examples where HaH has scaled. Beginning in 1994, in the state of Victoria, Australia (population 6.3 million), the health authority reimbursed HaH care at the same rates as traditional hospital care. At last report, HaH provided approximately 5% of all hospital bed days of care in Victoria. Providing HaH on this scale helped avoid the need to build a new 500-bed hospital to care for those patients.¹⁵ The avoided costs of building new hospital beds (and the ongoing need to fill those beds) represents significant societal return on investment attributable to HaH.

A key element in implementing a HaH program is its physician staff in terms of the types of doctors who provide HaH care, how they are organized, and how they interact with patients. To date, HaH physicians have been predominantly geriatricians, but internists and family medicine physicians, employed as full-time members of a dedicated HaH team, also provide care by physically visiting patients in their homes. The reason for significant involvement of geriatricians in HaH may relate to the fact that geriatric fellowship training includes training in home-based medical care, whereas this is less common in family medicine and internal medicine residency training programs.

In order to provide HaH on a nationwide scale, there will be a need for a larger workforce. There is an opportunity here to leverage existing hospital physician staff, such as hospitalists. In addition, while there is significant value in physicians seeing patients in their homes, more scalable versions of HaH are being developed and implemented that leverage biometrically enhanced telemedicine approaches for a dedicated physician component of care, with in-person visits provided by other members of an interdisciplinary team.

We believe that hospitalists can play a key role as HaH physicians as the HaH model continues to evolve and expand. Hospitalists bring valuable expertise relevant to HaH care delivery, including extensive experience with the triage of acutely ill patients, an understanding of the natural course of acute illness and team-based care, and for some, experience with telemedicine care.

While a hospitalist providing HaH care would leverage many of the competencies of the traditional hospitalist, we suggest that such a provider should receive additional training and clinical experience in home-based medical care to help them better understand the unique aspects of providing care in patients’ homes.¹⁶ Such training could include experience in making house calls, which can be a transformational experience in helping physicians improve their skills in dealing with social determinants of health, diagnosing and managing geriatric syndromes, and mobilizing community resources in the care of their patients, as well as managing care transitions. Hospitalists delivering care in HaH may also need to upgrade specific clinical skills commonly addressed by home-based medical care providers: wound care, caregiver-related issues, social and ethical issues specific to home-based care, problems with functional status, psychiatric and cognitive issues, management of gastrostomy tubes and bladder catheters, and dermatologic problems, as well as palliative care and end-of-life symptom management. These skills are slightly different from the usual realm of the typical hospitalists’ wheelhouse. However, it is all learnable.¹⁷ Similarly, geriatricians can learn from hospitalists as the HaH model evolves; there are HaH programs in existence today that take care of a sicker tranche of patients than earlier versions of HaH, with continuous telemonitoring of patients and the ability to rapidly deploy providers, labs, imaging, and medications. Going forward, as healthcare organizations begin to develop HaH programs staffed by hospitalists, it is probably wise for hospitalists and geriatricians to collaborate on the optimal physician models for HaH.

There may emerge a new specialty. Ticona and Schulman described a “home intensivist” with competencies including informatics of remote monitoring technology, leadership of multidisciplinary care teams, and the interpersonal skills required for compassionate end-of-life care.¹⁸ We prefer the term Home Hospitalist. Home Hospitalists would develop an enhanced understanding of the transitions of care and social determinants of health, and they would gain valuable knowledge about the social and environmental challenges many patients face after discharge from the hospital.

When this vision is realized, there will be enormous benefits to both HaH and Hospital Medicine. HaH could tap into a large and competent workforce to enhance its implementation and dissemination. Hospital Medicine would gain a new pathway for its providers and could develop new collaborative efforts with geriatric, internal, and family medicine.

Disclosures

Dr. Danielsson has nothing to disclose. Dr. Leff reports personal fees from Medically Home, other from Dispatch Health, other from Landmark Health, personal fees from Medibank, personal fees from Apple, personal fees from Health Affairs, other from Honor, personal fees from Institute for Healthcare Improvement, outside the submitted work; and American Academy of Home Care Medicine - member board of directors, voluntary.

Funding

Dr. Leff was supported in this work by a grant from The John A. Hartford Foundation.

Ms. P., an 86-year-old woman with a history of hypertension, hyperlipidemia, coronary artery disease, and transient ischemic attack, presents to the emergency department with a three-day history of cough, fever, purulent sputum, fatigue, and dyspnea on exertion. Her vital signs are notable for a fever of 39.0°C, blood pressure 136/92, pulse 102, respiratory rate 30, and room air oxygen saturation of 91%. She looks ill. She has a white blood cell count of 16,000, lactate 1.9, and a right lower lobe infiltrate on imaging. The emergency department attending physician presents the case to you for admission, and you accept the patient into your inpatient hospitalist service.

Now, let’s imagine a different future in which you are the attending hospitalist on your institution’s Hospital at Home (HaH) service, where you will provide hospital-level care to Ms. P. in the comfort of her own home. Hospitalists should prepare for this paradigm shift.

HaH provides hospital-level care in a patient’s home, for those with qualifying acute illnesses and appropriate degrees of acuity, as a substitute for traditional inpatient care.¹ This is achieved by bringing the critical elements of hospital care to the home—physician and nursing care, intravenous medications and fluids, oxygen and respiratory therapies, basic radiography and ultrasound, durable medical equipment, skilled therapies, and more.²

All hospitalists have cared for patients like Ms. P., and she and many patients like her will have a straightforward hospital trajectory: initial evaluation in the emergency department, inpatient care provided by a hospitalist inpatient service, a few days of intravenous antibiotics and other hospital services, and finally, discharge to home.

However, not all patients will experience a smooth, or safe, hospital course. Studies that launched the hospital safety movement also provide the rationale for HaH, namely, that hospitals are often dangerous environments for patients.³

A complementary approach to improving outcomes for patients at high risk of iatrogenic illness such as functional decline, falls, delirium, adverse drug events, and hospital-associated disability syndrome,^4-6 is to care for patients outside the traditional inpatient hospital environment. Over the past 20 years, many studies—including dozens of randomized controlled trials and several meta-analyses—have shown better outcomes for patients cared for in HaH: decreased length of stay, decreased incidence of adverse events (including substantially lower six-month mortality), better patient and caregiver care experiences, lower caregiver stress, and lower costs.^7-9A recent Center for Medicare and Medicaid Innovation (CMMI) Demonstration conducted at the Mount Sinai Health System found similar results.¹⁰

Interest in HaH has increased markedly over the past few years with increased penetration of Medicare and Medicaid managed care, the development and spread of accountable care organizations (ACOs), and a shift in focus among some health systems towards value-based care, population health, and community-based care. Recently, commercial entities have entered the HaH space and have raised substantial capital to fund development. Despite this growing interest in HaH and substantial evidence of its effectiveness, HaH has not been widely implemented or scaled in the United States.

Widespread dissemination and implementation of HaH has been hampered by several barriers. First, despite growing interest in HaH, the culture of healthcare and health system leadership, for the most part, remains focused on facility-based care.¹¹

Second, while HaH makes financial sense in the managed care arena, given the strong evidence for high-quality, lower-cost care, there is currently no standard payment mechanism for HaH in fee-for-service Medicare or in the commercial insurance space. However, there are indications that this may soon change. In the fall of 2017, a proposal for a bundled payment mechanism for acute HaH care plus 30 days of postacute care was unanimously approved by an Advisory Committee to the Secretary of the Department of Health and Human Services (HHS).^12,13 The HHS Secretary recently noted that “the Department of Health and Human Services is keenly interested in ideas for home-based, hospital-level care, and agrees … that this proposal holds promise for testing.”¹⁴

Third is the need to create the logistics and supply chain to support HaH. There currently exists a well-established supply chain for providing hospital care. A hospitalist orders a dose of intravenous antibiotic or oxygen, and it is supplied in a timely manner. Similarly, the postacute sector of healthcare has a robust supply chain, though it operates on a somewhat different clock from the acute care setting. However, there is currently no easily replicable supply chain to meet the needs of providing acute care in the home. Each HaH has had to create its own system of logistics with the existing healthcare assets in its local environment. Developing this capacity at scale will require significant capital investment.

There are examples where HaH has scaled. Beginning in 1994, in the state of Victoria, Australia (population 6.3 million), the health authority reimbursed HaH care at the same rates as traditional hospital care. At last report, HaH provided approximately 5% of all hospital bed days of care in Victoria. Providing HaH on this scale helped avoid the need to build a new 500-bed hospital to care for those patients.¹⁵ The avoided costs of building new hospital beds (and the ongoing need to fill those beds) represents significant societal return on investment attributable to HaH.

A key element in implementing a HaH program is its physician staff in terms of the types of doctors who provide HaH care, how they are organized, and how they interact with patients. To date, HaH physicians have been predominantly geriatricians, but internists and family medicine physicians, employed as full-time members of a dedicated HaH team, also provide care by physically visiting patients in their homes. The reason for significant involvement of geriatricians in HaH may relate to the fact that geriatric fellowship training includes training in home-based medical care, whereas this is less common in family medicine and internal medicine residency training programs.

In order to provide HaH on a nationwide scale, there will be a need for a larger workforce. There is an opportunity here to leverage existing hospital physician staff, such as hospitalists. In addition, while there is significant value in physicians seeing patients in their homes, more scalable versions of HaH are being developed and implemented that leverage biometrically enhanced telemedicine approaches for a dedicated physician component of care, with in-person visits provided by other members of an interdisciplinary team.

We believe that hospitalists can play a key role as HaH physicians as the HaH model continues to evolve and expand. Hospitalists bring valuable expertise relevant to HaH care delivery, including extensive experience with the triage of acutely ill patients, an understanding of the natural course of acute illness and team-based care, and for some, experience with telemedicine care.

While a hospitalist providing HaH care would leverage many of the competencies of the traditional hospitalist, we suggest that such a provider should receive additional training and clinical experience in home-based medical care to help them better understand the unique aspects of providing care in patients’ homes.¹⁶ Such training could include experience in making house calls, which can be a transformational experience in helping physicians improve their skills in dealing with social determinants of health, diagnosing and managing geriatric syndromes, and mobilizing community resources in the care of their patients, as well as managing care transitions. Hospitalists delivering care in HaH may also need to upgrade specific clinical skills commonly addressed by home-based medical care providers: wound care, caregiver-related issues, social and ethical issues specific to home-based care, problems with functional status, psychiatric and cognitive issues, management of gastrostomy tubes and bladder catheters, and dermatologic problems, as well as palliative care and end-of-life symptom management. These skills are slightly different from the usual realm of the typical hospitalists’ wheelhouse. However, it is all learnable.¹⁷ Similarly, geriatricians can learn from hospitalists as the HaH model evolves; there are HaH programs in existence today that take care of a sicker tranche of patients than earlier versions of HaH, with continuous telemonitoring of patients and the ability to rapidly deploy providers, labs, imaging, and medications. Going forward, as healthcare organizations begin to develop HaH programs staffed by hospitalists, it is probably wise for hospitalists and geriatricians to collaborate on the optimal physician models for HaH.

There may emerge a new specialty. Ticona and Schulman described a “home intensivist” with competencies including informatics of remote monitoring technology, leadership of multidisciplinary care teams, and the interpersonal skills required for compassionate end-of-life care.¹⁸ We prefer the term Home Hospitalist. Home Hospitalists would develop an enhanced understanding of the transitions of care and social determinants of health, and they would gain valuable knowledge about the social and environmental challenges many patients face after discharge from the hospital.

When this vision is realized, there will be enormous benefits to both HaH and Hospital Medicine. HaH could tap into a large and competent workforce to enhance its implementation and dissemination. Hospital Medicine would gain a new pathway for its providers and could develop new collaborative efforts with geriatric, internal, and family medicine.

Disclosures

Dr. Danielsson has nothing to disclose. Dr. Leff reports personal fees from Medically Home, other from Dispatch Health, other from Landmark Health, personal fees from Medibank, personal fees from Apple, personal fees from Health Affairs, other from Honor, personal fees from Institute for Healthcare Improvement, outside the submitted work; and American Academy of Home Care Medicine - member board of directors, voluntary.

Funding

Dr. Leff was supported in this work by a grant from The John A. Hartford Foundation.

Ms. P., an 86-year-old woman with a history of hypertension, hyperlipidemia, coronary artery disease, and transient ischemic attack, presents to the emergency department with a three-day history of cough, fever, purulent sputum, fatigue, and dyspnea on exertion. Her vital signs are notable for a fever of 39.0°C, blood pressure 136/92, pulse 102, respiratory rate 30, and room air oxygen saturation of 91%. She looks ill. She has a white blood cell count of 16,000, lactate 1.9, and a right lower lobe infiltrate on imaging. The emergency department attending physician presents the case to you for admission, and you accept the patient into your inpatient hospitalist service.

Now, let’s imagine a different future in which you are the attending hospitalist on your institution’s Hospital at Home (HaH) service, where you will provide hospital-level care to Ms. P. in the comfort of her own home. Hospitalists should prepare for this paradigm shift.

HaH provides hospital-level care in a patient’s home, for those with qualifying acute illnesses and appropriate degrees of acuity, as a substitute for traditional inpatient care.¹ This is achieved by bringing the critical elements of hospital care to the home—physician and nursing care, intravenous medications and fluids, oxygen and respiratory therapies, basic radiography and ultrasound, durable medical equipment, skilled therapies, and more.²

All hospitalists have cared for patients like Ms. P., and she and many patients like her will have a straightforward hospital trajectory: initial evaluation in the emergency department, inpatient care provided by a hospitalist inpatient service, a few days of intravenous antibiotics and other hospital services, and finally, discharge to home.

However, not all patients will experience a smooth, or safe, hospital course. Studies that launched the hospital safety movement also provide the rationale for HaH, namely, that hospitals are often dangerous environments for patients.³

A complementary approach to improving outcomes for patients at high risk of iatrogenic illness such as functional decline, falls, delirium, adverse drug events, and hospital-associated disability syndrome,^4-6 is to care for patients outside the traditional inpatient hospital environment. Over the past 20 years, many studies—including dozens of randomized controlled trials and several meta-analyses—have shown better outcomes for patients cared for in HaH: decreased length of stay, decreased incidence of adverse events (including substantially lower six-month mortality), better patient and caregiver care experiences, lower caregiver stress, and lower costs.^7-9A recent Center for Medicare and Medicaid Innovation (CMMI) Demonstration conducted at the Mount Sinai Health System found similar results.¹⁰

Interest in HaH has increased markedly over the past few years with increased penetration of Medicare and Medicaid managed care, the development and spread of accountable care organizations (ACOs), and a shift in focus among some health systems towards value-based care, population health, and community-based care. Recently, commercial entities have entered the HaH space and have raised substantial capital to fund development. Despite this growing interest in HaH and substantial evidence of its effectiveness, HaH has not been widely implemented or scaled in the United States.

Widespread dissemination and implementation of HaH has been hampered by several barriers. First, despite growing interest in HaH, the culture of healthcare and health system leadership, for the most part, remains focused on facility-based care.¹¹

Second, while HaH makes financial sense in the managed care arena, given the strong evidence for high-quality, lower-cost care, there is currently no standard payment mechanism for HaH in fee-for-service Medicare or in the commercial insurance space. However, there are indications that this may soon change. In the fall of 2017, a proposal for a bundled payment mechanism for acute HaH care plus 30 days of postacute care was unanimously approved by an Advisory Committee to the Secretary of the Department of Health and Human Services (HHS).^12,13 The HHS Secretary recently noted that “the Department of Health and Human Services is keenly interested in ideas for home-based, hospital-level care, and agrees … that this proposal holds promise for testing.”¹⁴

Third is the need to create the logistics and supply chain to support HaH. There currently exists a well-established supply chain for providing hospital care. A hospitalist orders a dose of intravenous antibiotic or oxygen, and it is supplied in a timely manner. Similarly, the postacute sector of healthcare has a robust supply chain, though it operates on a somewhat different clock from the acute care setting. However, there is currently no easily replicable supply chain to meet the needs of providing acute care in the home. Each HaH has had to create its own system of logistics with the existing healthcare assets in its local environment. Developing this capacity at scale will require significant capital investment.

There are examples where HaH has scaled. Beginning in 1994, in the state of Victoria, Australia (population 6.3 million), the health authority reimbursed HaH care at the same rates as traditional hospital care. At last report, HaH provided approximately 5% of all hospital bed days of care in Victoria. Providing HaH on this scale helped avoid the need to build a new 500-bed hospital to care for those patients.¹⁵ The avoided costs of building new hospital beds (and the ongoing need to fill those beds) represents significant societal return on investment attributable to HaH.

A key element in implementing a HaH program is its physician staff in terms of the types of doctors who provide HaH care, how they are organized, and how they interact with patients. To date, HaH physicians have been predominantly geriatricians, but internists and family medicine physicians, employed as full-time members of a dedicated HaH team, also provide care by physically visiting patients in their homes. The reason for significant involvement of geriatricians in HaH may relate to the fact that geriatric fellowship training includes training in home-based medical care, whereas this is less common in family medicine and internal medicine residency training programs.

In order to provide HaH on a nationwide scale, there will be a need for a larger workforce. There is an opportunity here to leverage existing hospital physician staff, such as hospitalists. In addition, while there is significant value in physicians seeing patients in their homes, more scalable versions of HaH are being developed and implemented that leverage biometrically enhanced telemedicine approaches for a dedicated physician component of care, with in-person visits provided by other members of an interdisciplinary team.

We believe that hospitalists can play a key role as HaH physicians as the HaH model continues to evolve and expand. Hospitalists bring valuable expertise relevant to HaH care delivery, including extensive experience with the triage of acutely ill patients, an understanding of the natural course of acute illness and team-based care, and for some, experience with telemedicine care.

While a hospitalist providing HaH care would leverage many of the competencies of the traditional hospitalist, we suggest that such a provider should receive additional training and clinical experience in home-based medical care to help them better understand the unique aspects of providing care in patients’ homes.¹⁶ Such training could include experience in making house calls, which can be a transformational experience in helping physicians improve their skills in dealing with social determinants of health, diagnosing and managing geriatric syndromes, and mobilizing community resources in the care of their patients, as well as managing care transitions. Hospitalists delivering care in HaH may also need to upgrade specific clinical skills commonly addressed by home-based medical care providers: wound care, caregiver-related issues, social and ethical issues specific to home-based care, problems with functional status, psychiatric and cognitive issues, management of gastrostomy tubes and bladder catheters, and dermatologic problems, as well as palliative care and end-of-life symptom management. These skills are slightly different from the usual realm of the typical hospitalists’ wheelhouse. However, it is all learnable.¹⁷ Similarly, geriatricians can learn from hospitalists as the HaH model evolves; there are HaH programs in existence today that take care of a sicker tranche of patients than earlier versions of HaH, with continuous telemonitoring of patients and the ability to rapidly deploy providers, labs, imaging, and medications. Going forward, as healthcare organizations begin to develop HaH programs staffed by hospitalists, it is probably wise for hospitalists and geriatricians to collaborate on the optimal physician models for HaH.

There may emerge a new specialty. Ticona and Schulman described a “home intensivist” with competencies including informatics of remote monitoring technology, leadership of multidisciplinary care teams, and the interpersonal skills required for compassionate end-of-life care.¹⁸ We prefer the term Home Hospitalist. Home Hospitalists would develop an enhanced understanding of the transitions of care and social determinants of health, and they would gain valuable knowledge about the social and environmental challenges many patients face after discharge from the hospital.

When this vision is realized, there will be enormous benefits to both HaH and Hospital Medicine. HaH could tap into a large and competent workforce to enhance its implementation and dissemination. Hospital Medicine would gain a new pathway for its providers and could develop new collaborative efforts with geriatric, internal, and family medicine.

Disclosures

Dr. Danielsson has nothing to disclose. Dr. Leff reports personal fees from Medically Home, other from Dispatch Health, other from Landmark Health, personal fees from Medibank, personal fees from Apple, personal fees from Health Affairs, other from Honor, personal fees from Institute for Healthcare Improvement, outside the submitted work; and American Academy of Home Care Medicine - member board of directors, voluntary.

Funding

Dr. Leff was supported in this work by a grant from The John A. Hartford Foundation.