A 50‐year‐old man presented to the emergency department with progressive shortness of breath for 6 months. He described a dry cough, left‐sided chest pain, malaise, night sweats, and a 15‐pound weight loss. The patient had never smoked cigarettes, but he had been exposed to asbestos and wood dust when working at a sawmill. His physical examination was remarkable for decreased breath sounds at the left lung base. The admission blood tests were within normal limits. Chest radiography and a computed tomography (CT) scan of the chest were performed (the CT scan is shown in Figures 1 and 2). The CT scan showed a left pleural effusion with subpleural and peribronchovascular nodules. Also demonstrated on the CT scan were bilateral hilar and mediastinal lymphadenopathies with faint central calcification. As the left‐sided pleural effusion was initially suspected to be malignant, a thoracentesis was performed, and it revealed an exudative effusion. The total white cell count in fluid was 2100/L (lymphocytes, 76%), and cultures for aerobic and anaerobic bacteria, acid fasting bacilli, and fungi were negative. Cytology was negative for malignant cells. On the basis of the findings in the lung parenchyma and the presence of mediastinal lymphadenopathies, fiber‐optic bronchoscopy with bronchoalveolar lavage, protected specimen brushing, transbronchial needle aspiration, and transbronchial biopsies were performed. Mediastinal lymph node cytology was negative for malignant cells, whereas transbronchial biopsies showed noncaseating granulomas (Figure 3). At that time, our differential diagnoses of noncaseating granulomas included mycobacterium infection (although this usually presents caseating granulomas), berylliosis, histoplasmosis, and sarcoidosis. The tuberculin skin test (purified protein derivative) and serology for human immunodeficiency virus were negative. Bronchoalveolar lavage and cultures of lung tissue biopsies as well as needle aspiration from mediastinal lymph nodes were negative for mycobacterial, fungal, and bacterial organisms. The beryllium lymphocyte proliferation test was normal. Serologic antibodies for Aspergillus, Blastomyces, Coccidioides, and Histoplasma were negative. The urinary Histoplasma antigen was negative as well. The Department of Infectious Diseases was consulted, and an empirical treatment for histoplasmosis with itraconazole was started on the basis of the residence of the patient and the presence of noncaseating granulomas. After 1 month of antifungal treatment, there was no significant improvement. Video‐assisted thoracoscopic surgery with pleural biopsy was performed because of persistent pleural effusion and concern about an underlying infectious or malignant process. Pleural biopsies showed noncaseating granulomas (Figure 4). Pleural fluid was sent for adenosine deaminase (17 U/L) and flow cytometry (CD4/CD8 2.71). Cultures and cytology remained negative. A diagnosis of stage 2 sarcoidosis with pleural involvement was made, and treatment with prednisone was started.

Figure 1

Figure 2

Figure 3

Figure 4

Discussion

The overall prevalence of pleural involvement in sarcoidosis is about 3%. Patients with pleural sarcoidosis tend to be between 30 and 50 years of age, in contrast to the usual presentation of sarcoidosis between 20 and 30 years of age. The most common forms of pleural involvement are pleural effusions, pneumothorax, pleural thickening, and pleural nodules.1 Most effusions are usually small or modest in size, with few reports describing massive effusions.2 Recurrent pleural and pericardial effusions due to sarcoidosis have been reported as well.3 The fluid is typically a lymphocytic exudate, and almost all cases describe a CD4 predominant lymphocytic effusion with CD4/CD8 ratios ranging from 2.35 to 8.6.1 The presence of bloody pleural effusions in sarcoidosis most likely represents the rupture of small vessels that are compressed or infiltrated by granulomas.4

The majority of patients with reported sarcoid pleural effusions have stage 2 disease. With the progression of parenchymal disease, the prevalence of pleural effusions decreases, whereas pleural thickening and pneumothorax increase.5 It is important to emphasize that 40% of pleural effusions in sarcoidosis may be due to other causes, such as tuberculosis and mesothelioma. Our patient was initially treated with itraconazole as histoplasmosis is most prevalent in the Central and Eastern United States, especially in Ohio River valleys, where this patient lived.

The prevalence of a pneumothorax in sarcoidosis is up to 4%.1 Pleural thickening can be demonstrated in 11% to 71% of patients with pleural sarcoidosis, and 10% to 20% of these cases have thickening without effusion. Detection of subpleural nodules and cysts has been possible since the introduction of high‐resolution CT scans. Their prevalence in sarcoidosis ranges from 22% to 76%, and they are often described as masses that correspond to nodules seen in both parietal and visceral surfaces. Hilar or mediastinal lymphadenopathy is present on CT in 47% to 94% of patients with sarcoidosis. Lymph node enlargement is usually bilateral, most commonly with right‐sided predominance. The most involved stations are the right lower paratracheal, right hilar, subcarinal, aortopulmonary window, and right interlobar stations. Nodal calcification is noted in 53% with eggshell calcification present in 9%. The enlargement of internal mammary and pericardial lymph nodes requires the exclusion of lymphoma.6

The management of pleural sarcoidosis should be individualized because a majority of these effusions resolve spontaneously in 1 to 3 months.5 There have been reports of resolution in 2 weeks with steroid therapy. Incomplete resolution of the pleural effusions with progression to chronic pleural thickening or a trapped lung has been reported. There is agreement that oral corticosteroid treatment should be considered in patients with severe persistent or progressively worsening respiratory symptoms or declining lung function. Severe symptoms can be considered as those that interfere with essential aspects of the patient's daily life.7 The initial dosage of oral prednisone recommended by the American Thoracic Society, the European Respiratory Society, and the World Association of Sarcoidosis and Other Granulomatous Disorders guidelines is 20 to 40 mg/day.8 Further evaluation is recommended after 1 to 3 months. If the patient responds, the dose should be reduced gradually to a maintenance dose, such as 5 to 15 mg/day of prednisolone. American Thoracic Society/European Respiratory Society/World Association of Sarcoidosis and Other Granulomatous Disorders guidelines advise treatment for at least 1 year. Immunosuppressive, cytotoxic, and immunomodulatory agents have been used to treat patients failing or experiencing adverse effects of steroids. Favorable responses have been reported with methotrexate, leflunomide, azathioprine, cyclophosphamide, chlorambucil, cyclosporine A, antimalarials, tumor necrosis factor inhibitors, and thalidomide. Because of potential serious toxicities associated with cyclophosphamide and chlorambucil, these agents are not recommended.9

Our patient presented with pleural sarcoidosis with a pleural effusion and nodules. Treatment with 20 mg of prednisone daily was started initially. Four weeks after discharge, he was still dyspneic and had persistent left pleural effusion. He also had gained a significant amount of weight and developed bilateral lower extremity edema; these were thought to be secondary to prednisone treatment. Steroids were subsequently tapered, and leflunomide was started. His symptoms improved dramatically after 1 month of treatment with leflunomide and steroids, and 3 months later, his pleural effusion had completely resolved.

A 50‐year‐old man presented to the emergency department with progressive shortness of breath for 6 months. He described a dry cough, left‐sided chest pain, malaise, night sweats, and a 15‐pound weight loss. The patient had never smoked cigarettes, but he had been exposed to asbestos and wood dust when working at a sawmill. His physical examination was remarkable for decreased breath sounds at the left lung base. The admission blood tests were within normal limits. Chest radiography and a computed tomography (CT) scan of the chest were performed (the CT scan is shown in Figures 1 and 2). The CT scan showed a left pleural effusion with subpleural and peribronchovascular nodules. Also demonstrated on the CT scan were bilateral hilar and mediastinal lymphadenopathies with faint central calcification. As the left‐sided pleural effusion was initially suspected to be malignant, a thoracentesis was performed, and it revealed an exudative effusion. The total white cell count in fluid was 2100/L (lymphocytes, 76%), and cultures for aerobic and anaerobic bacteria, acid fasting bacilli, and fungi were negative. Cytology was negative for malignant cells. On the basis of the findings in the lung parenchyma and the presence of mediastinal lymphadenopathies, fiber‐optic bronchoscopy with bronchoalveolar lavage, protected specimen brushing, transbronchial needle aspiration, and transbronchial biopsies were performed. Mediastinal lymph node cytology was negative for malignant cells, whereas transbronchial biopsies showed noncaseating granulomas (Figure 3). At that time, our differential diagnoses of noncaseating granulomas included mycobacterium infection (although this usually presents caseating granulomas), berylliosis, histoplasmosis, and sarcoidosis. The tuberculin skin test (purified protein derivative) and serology for human immunodeficiency virus were negative. Bronchoalveolar lavage and cultures of lung tissue biopsies as well as needle aspiration from mediastinal lymph nodes were negative for mycobacterial, fungal, and bacterial organisms. The beryllium lymphocyte proliferation test was normal. Serologic antibodies for Aspergillus, Blastomyces, Coccidioides, and Histoplasma were negative. The urinary Histoplasma antigen was negative as well. The Department of Infectious Diseases was consulted, and an empirical treatment for histoplasmosis with itraconazole was started on the basis of the residence of the patient and the presence of noncaseating granulomas. After 1 month of antifungal treatment, there was no significant improvement. Video‐assisted thoracoscopic surgery with pleural biopsy was performed because of persistent pleural effusion and concern about an underlying infectious or malignant process. Pleural biopsies showed noncaseating granulomas (Figure 4). Pleural fluid was sent for adenosine deaminase (17 U/L) and flow cytometry (CD4/CD8 2.71). Cultures and cytology remained negative. A diagnosis of stage 2 sarcoidosis with pleural involvement was made, and treatment with prednisone was started.

Figure 1

Figure 2

Figure 3

Figure 4

Discussion

The overall prevalence of pleural involvement in sarcoidosis is about 3%. Patients with pleural sarcoidosis tend to be between 30 and 50 years of age, in contrast to the usual presentation of sarcoidosis between 20 and 30 years of age. The most common forms of pleural involvement are pleural effusions, pneumothorax, pleural thickening, and pleural nodules.1 Most effusions are usually small or modest in size, with few reports describing massive effusions.2 Recurrent pleural and pericardial effusions due to sarcoidosis have been reported as well.3 The fluid is typically a lymphocytic exudate, and almost all cases describe a CD4 predominant lymphocytic effusion with CD4/CD8 ratios ranging from 2.35 to 8.6.1 The presence of bloody pleural effusions in sarcoidosis most likely represents the rupture of small vessels that are compressed or infiltrated by granulomas.4

The majority of patients with reported sarcoid pleural effusions have stage 2 disease. With the progression of parenchymal disease, the prevalence of pleural effusions decreases, whereas pleural thickening and pneumothorax increase.5 It is important to emphasize that 40% of pleural effusions in sarcoidosis may be due to other causes, such as tuberculosis and mesothelioma. Our patient was initially treated with itraconazole as histoplasmosis is most prevalent in the Central and Eastern United States, especially in Ohio River valleys, where this patient lived.

The prevalence of a pneumothorax in sarcoidosis is up to 4%.1 Pleural thickening can be demonstrated in 11% to 71% of patients with pleural sarcoidosis, and 10% to 20% of these cases have thickening without effusion. Detection of subpleural nodules and cysts has been possible since the introduction of high‐resolution CT scans. Their prevalence in sarcoidosis ranges from 22% to 76%, and they are often described as masses that correspond to nodules seen in both parietal and visceral surfaces. Hilar or mediastinal lymphadenopathy is present on CT in 47% to 94% of patients with sarcoidosis. Lymph node enlargement is usually bilateral, most commonly with right‐sided predominance. The most involved stations are the right lower paratracheal, right hilar, subcarinal, aortopulmonary window, and right interlobar stations. Nodal calcification is noted in 53% with eggshell calcification present in 9%. The enlargement of internal mammary and pericardial lymph nodes requires the exclusion of lymphoma.6

The management of pleural sarcoidosis should be individualized because a majority of these effusions resolve spontaneously in 1 to 3 months.5 There have been reports of resolution in 2 weeks with steroid therapy. Incomplete resolution of the pleural effusions with progression to chronic pleural thickening or a trapped lung has been reported. There is agreement that oral corticosteroid treatment should be considered in patients with severe persistent or progressively worsening respiratory symptoms or declining lung function. Severe symptoms can be considered as those that interfere with essential aspects of the patient's daily life.7 The initial dosage of oral prednisone recommended by the American Thoracic Society, the European Respiratory Society, and the World Association of Sarcoidosis and Other Granulomatous Disorders guidelines is 20 to 40 mg/day.8 Further evaluation is recommended after 1 to 3 months. If the patient responds, the dose should be reduced gradually to a maintenance dose, such as 5 to 15 mg/day of prednisolone. American Thoracic Society/European Respiratory Society/World Association of Sarcoidosis and Other Granulomatous Disorders guidelines advise treatment for at least 1 year. Immunosuppressive, cytotoxic, and immunomodulatory agents have been used to treat patients failing or experiencing adverse effects of steroids. Favorable responses have been reported with methotrexate, leflunomide, azathioprine, cyclophosphamide, chlorambucil, cyclosporine A, antimalarials, tumor necrosis factor inhibitors, and thalidomide. Because of potential serious toxicities associated with cyclophosphamide and chlorambucil, these agents are not recommended.9

Our patient presented with pleural sarcoidosis with a pleural effusion and nodules. Treatment with 20 mg of prednisone daily was started initially. Four weeks after discharge, he was still dyspneic and had persistent left pleural effusion. He also had gained a significant amount of weight and developed bilateral lower extremity edema; these were thought to be secondary to prednisone treatment. Steroids were subsequently tapered, and leflunomide was started. His symptoms improved dramatically after 1 month of treatment with leflunomide and steroids, and 3 months later, his pleural effusion had completely resolved.

A 50‐year‐old man presented to the emergency department with progressive shortness of breath for 6 months. He described a dry cough, left‐sided chest pain, malaise, night sweats, and a 15‐pound weight loss. The patient had never smoked cigarettes, but he had been exposed to asbestos and wood dust when working at a sawmill. His physical examination was remarkable for decreased breath sounds at the left lung base. The admission blood tests were within normal limits. Chest radiography and a computed tomography (CT) scan of the chest were performed (the CT scan is shown in Figures 1 and 2). The CT scan showed a left pleural effusion with subpleural and peribronchovascular nodules. Also demonstrated on the CT scan were bilateral hilar and mediastinal lymphadenopathies with faint central calcification. As the left‐sided pleural effusion was initially suspected to be malignant, a thoracentesis was performed, and it revealed an exudative effusion. The total white cell count in fluid was 2100/L (lymphocytes, 76%), and cultures for aerobic and anaerobic bacteria, acid fasting bacilli, and fungi were negative. Cytology was negative for malignant cells. On the basis of the findings in the lung parenchyma and the presence of mediastinal lymphadenopathies, fiber‐optic bronchoscopy with bronchoalveolar lavage, protected specimen brushing, transbronchial needle aspiration, and transbronchial biopsies were performed. Mediastinal lymph node cytology was negative for malignant cells, whereas transbronchial biopsies showed noncaseating granulomas (Figure 3). At that time, our differential diagnoses of noncaseating granulomas included mycobacterium infection (although this usually presents caseating granulomas), berylliosis, histoplasmosis, and sarcoidosis. The tuberculin skin test (purified protein derivative) and serology for human immunodeficiency virus were negative. Bronchoalveolar lavage and cultures of lung tissue biopsies as well as needle aspiration from mediastinal lymph nodes were negative for mycobacterial, fungal, and bacterial organisms. The beryllium lymphocyte proliferation test was normal. Serologic antibodies for Aspergillus, Blastomyces, Coccidioides, and Histoplasma were negative. The urinary Histoplasma antigen was negative as well. The Department of Infectious Diseases was consulted, and an empirical treatment for histoplasmosis with itraconazole was started on the basis of the residence of the patient and the presence of noncaseating granulomas. After 1 month of antifungal treatment, there was no significant improvement. Video‐assisted thoracoscopic surgery with pleural biopsy was performed because of persistent pleural effusion and concern about an underlying infectious or malignant process. Pleural biopsies showed noncaseating granulomas (Figure 4). Pleural fluid was sent for adenosine deaminase (17 U/L) and flow cytometry (CD4/CD8 2.71). Cultures and cytology remained negative. A diagnosis of stage 2 sarcoidosis with pleural involvement was made, and treatment with prednisone was started.

Figure 1

Figure 2

Figure 3

Figure 4

Discussion

The overall prevalence of pleural involvement in sarcoidosis is about 3%. Patients with pleural sarcoidosis tend to be between 30 and 50 years of age, in contrast to the usual presentation of sarcoidosis between 20 and 30 years of age. The most common forms of pleural involvement are pleural effusions, pneumothorax, pleural thickening, and pleural nodules.1 Most effusions are usually small or modest in size, with few reports describing massive effusions.2 Recurrent pleural and pericardial effusions due to sarcoidosis have been reported as well.3 The fluid is typically a lymphocytic exudate, and almost all cases describe a CD4 predominant lymphocytic effusion with CD4/CD8 ratios ranging from 2.35 to 8.6.1 The presence of bloody pleural effusions in sarcoidosis most likely represents the rupture of small vessels that are compressed or infiltrated by granulomas.4

The majority of patients with reported sarcoid pleural effusions have stage 2 disease. With the progression of parenchymal disease, the prevalence of pleural effusions decreases, whereas pleural thickening and pneumothorax increase.5 It is important to emphasize that 40% of pleural effusions in sarcoidosis may be due to other causes, such as tuberculosis and mesothelioma. Our patient was initially treated with itraconazole as histoplasmosis is most prevalent in the Central and Eastern United States, especially in Ohio River valleys, where this patient lived.

The prevalence of a pneumothorax in sarcoidosis is up to 4%.1 Pleural thickening can be demonstrated in 11% to 71% of patients with pleural sarcoidosis, and 10% to 20% of these cases have thickening without effusion. Detection of subpleural nodules and cysts has been possible since the introduction of high‐resolution CT scans. Their prevalence in sarcoidosis ranges from 22% to 76%, and they are often described as masses that correspond to nodules seen in both parietal and visceral surfaces. Hilar or mediastinal lymphadenopathy is present on CT in 47% to 94% of patients with sarcoidosis. Lymph node enlargement is usually bilateral, most commonly with right‐sided predominance. The most involved stations are the right lower paratracheal, right hilar, subcarinal, aortopulmonary window, and right interlobar stations. Nodal calcification is noted in 53% with eggshell calcification present in 9%. The enlargement of internal mammary and pericardial lymph nodes requires the exclusion of lymphoma.6

The management of pleural sarcoidosis should be individualized because a majority of these effusions resolve spontaneously in 1 to 3 months.5 There have been reports of resolution in 2 weeks with steroid therapy. Incomplete resolution of the pleural effusions with progression to chronic pleural thickening or a trapped lung has been reported. There is agreement that oral corticosteroid treatment should be considered in patients with severe persistent or progressively worsening respiratory symptoms or declining lung function. Severe symptoms can be considered as those that interfere with essential aspects of the patient's daily life.7 The initial dosage of oral prednisone recommended by the American Thoracic Society, the European Respiratory Society, and the World Association of Sarcoidosis and Other Granulomatous Disorders guidelines is 20 to 40 mg/day.8 Further evaluation is recommended after 1 to 3 months. If the patient responds, the dose should be reduced gradually to a maintenance dose, such as 5 to 15 mg/day of prednisolone. American Thoracic Society/European Respiratory Society/World Association of Sarcoidosis and Other Granulomatous Disorders guidelines advise treatment for at least 1 year. Immunosuppressive, cytotoxic, and immunomodulatory agents have been used to treat patients failing or experiencing adverse effects of steroids. Favorable responses have been reported with methotrexate, leflunomide, azathioprine, cyclophosphamide, chlorambucil, cyclosporine A, antimalarials, tumor necrosis factor inhibitors, and thalidomide. Because of potential serious toxicities associated with cyclophosphamide and chlorambucil, these agents are not recommended.9

Our patient presented with pleural sarcoidosis with a pleural effusion and nodules. Treatment with 20 mg of prednisone daily was started initially. Four weeks after discharge, he was still dyspneic and had persistent left pleural effusion. He also had gained a significant amount of weight and developed bilateral lower extremity edema; these were thought to be secondary to prednisone treatment. Steroids were subsequently tapered, and leflunomide was started. His symptoms improved dramatically after 1 month of treatment with leflunomide and steroids, and 3 months later, his pleural effusion had completely resolved.

I was 12 years old before I knew her actual name, Le Thi Canh, because we always called her Ba ngoai. She was my grandmother, and this is the story of how she died.

To see my grandmother for the warrior woman that she was to me, you have to know that her farmer father sent her to the city for schooling because he didn't know what else to do with a daughter who was so smart. In early 20th‐century Vietnam, this was an unusual thing to do with a girl. She met my grandfather there, when he was a campus activist, helping him hand out nationalist leaflets. He introduced her to his Communist friends. After the French jailed my grandfather, my grandmother courted him by sending him long letters and care packages while he was in prison. When he was finally released, they married and started a family while he struggled financially as a newspaper publisher and at other odd jobs. But in 1947 his own Communist comrades killed him as part of a party purge. He had been forewarned, and opted to go quietly rather than try to escape because he was promised that this would guarantee her safety. Before they killed him, somewhere in the mountains, my grandfather gave a soldier friend a poem he wrote for his wife. When she told me this story 8 years ago, more than half a century later, she recited his love missive from memory.

At the time of her husband's death, my grandmother had 6 children, the last born just a few weeks before. After a few years of scraping by (she ran her own one‐room school for a while), she decided to leave Hanoi, and migrated south to Saigon with her brood. She was a famously strict parent, to hear my aunts and uncles tell it. She watched them like a hawk, worked full‐time, put them all through school, and eventually rose to a leadership position in the Ministry of Social Work in South Vietnam. My memories of Saigon life are punctuated by scenes of siblings and cousins running around at her regal house, yellow stucco with porticos and black iron gates, at a corner turn in the road, past a cemetery.

On this side of the world, to see her, you would never have thought that my grandmother had led such an epic life. She never worked again after immigrating with us in 1975. She lived on Social Security checks, gardened, said Buddhist prayers, and was nanny to her grandchildren. She watched soap operas religiously, and could report their full plot lines while sitting and knitting. She bundled her many sadnesses in a contented, 4‐foot 9‐inch frame.

Having no home of her own, she would move from one child's house to another every few months so as not to wear out her welcome. But her children lived in Pennsylvania, New Jersey, Florida, Texas, and Maryland. And in most of these cities, she had a different primary care physician. She has 21 grandchildren; 8 of us are physicians. Yet the aunts and uncles told us very little about her medical care. She preferred older‐generation Vietnamese physicians and I'm not sure that they were all competent, but her children did not want to argue with an octogenarian war survivor, and we deferred to their judgment. So we would find out only incidentally, for example, that a doctor prescribed her tuberculosis drugs for a visit to Vietnam.

For many years, Ba ngoai had no major medical problems. She was hypothyroid and hypertensive but on medication and generally high functioning. She had a lumpectomy for early‐stage breast cancer. Then, a year or so after she told me the story of my grandfather's death, Alzheimer's set in. It became harder for her to report symptoms reliably, and she became mildly depressed. Her grandchildren were now birthing our own babies, and we offered these as a distraction, trying to surround her with celebrations of these new fruits of her life labors.

Ba ngoai's decline worsened 3 years ago. She became more easily fatigued, depressed, and confused. A few months before she died, she started to get dyspneic, and couldn't go for short walks any more. In retrospect, I think that her prescription for thyroid replacement somehow fell through the cracks, probably in the transfer of care from one city to another, although there remains a great deal of confusion in the family about exactly what happened. Her thyroxine levels dwindled. One evening in October of that year, at my uncle's house in Maryland, she became severely short of breath and nearly unconscious. They called her Maryland PCP, who sent her to an emergency room at a local hospital. She was admitted in severe congestive heart failure. When the hospitalist spoke with my mother and uncle, he explained that he could take some fluid off her lungs, but that she might need to be intubated and admitted to intensive care. Looking back, I guessed that she probably needed pressors and invasive monitoring. He asked them, Is this what you want? My uncle said, No, it's not. And the hospitalist and the huddle of relatives decided she should come home.

The question was, into whose care would she be discharged? My elders were wary of contacting her PCP, partly because some blamed him for not catching and addressing her symptoms sooner, partly because to even confront him with this perception would cause him, and hence them, to lose face. This seemed too excruciating a scenario to them.

So at last, my uncle called my brother, the oldest grandchild and a very talented clinician. My brother is a pulmonologist, the kind of physician who once did a history and physical on a patient complaining only of Really feeling bad, Doc, and confidently started a steroid infusion before returning the next day with test results confirming his suspicion of Wegener's granulomatosis. He took my grandmother's physical by phone, and told my uncle to increase her furosemide dose. Then he said, I'm on call, but I'll be down there tomorrow. Call everyone together. Most of my relatives were already in town; they had come at news of her decline. She became alert enough for a couple of days to see and recognize most of the faces around her, like so many markers on a long journey. And then she died, slipped away.

I find it hard to define good coordination of care. My instinct as a researcher is to list measurable elements, but the tools we currently have generate metrics that are either reductionistsuch as how rapidly a physician returns a patient's callor so global that they no longer seem actionablesuch as patient satisfaction. But if such metrics set the goal in the distance, it seems useful to also define its counterpartdiscoordinationas a marker of the reality we would like to leave behind us as far as possible. Discoordination includes elements of discontinuity (lost patient history), fragmentation (actions by multiple players), overuse and/or inappropriate use of services, and ultimately, ineffective care (that is, the patient's needs go unmet).

My experience of discoordination was that of a Rube Goldberg contraption. It's composed of innumerable subtasks, each cleverly designed as the easiest solution to a seemingly short‐term problem, as quick fixes, but that in aggregate generate such chaos that the ultimate purpose is lost. They include acts of denial, lies to avoid embarrassment or conflict, and choices of convenience. My mother and her siblings accommodated my grandmother's choice of physicians by (secretly) not always adhering to care recommendations they didn't agree with, instead of challenging her. They took her to different physicians in different cities rather than risk embarrassing (due to an exaggerated sense of the smallness of the Vietnamese community) any one physician by dropping him. Her grandchildren, despite our medical training, found it culturally easier to defer to our elders than to intervene in substandard care. And none of her physicians aggressively followed up to ensure that a frail Alzheimer's patient was getting the care she needed. This is not to suggest that coordination is a simple task because Rube Goldberg machines make simple tasks complicated. Rather, it is a depiction of how indirectly we tend to address the problem.

I imagine a different course of events for my grandmother in the absence of discoordination. What if her children and physicians had understood and acknowledged to one another that her care was fragmented and therefore suboptimal? What if we grandchildren had confronted both Ba Ngoai and our parents sooner about their choice of physicians and offered to take on more of the burden of helping with her care decisions? Would we, as physicians, have been better able to ensure that her providers made rational clinical decisions? And what if she and her family had consistently recognized a single physician as her medical home? Snowbirding is hardly a rare phenomenon among Medicare patients; we could have designated one physician as primarily responsible for coordinating her care even without limiting her travel.

Care coordination is an inherently human activity. Supportive elements such as efficient transfer of medical information, resources for patient education and self‐care, and adequate reimbursement can take us to the brink of, but not actually bridge, the chasm that we want to cross. Traversing that divide sometimes requires settling turf issues over undesirable responsibilities between different physicians and between physicians and other providers; clarifying who has primary responsibility for different types of decisions (I lead on cardiac issues and her son leads on health maintenance); and the violation of cultural norms of patients, families, and/or providers. These can be uncomfortable, unpleasant conversations that at times seem beside the point. But in aggregate, they are the work of coordination, because they force us to align our expectations of one another. No level of information technology could have dismantled the Rube Goldberg machine that trapped my grandmother. Her last of many lessons for me was that emotional courage, honesty, and perseverance offer a much more direct path through the muck.

I was 12 years old before I knew her actual name, Le Thi Canh, because we always called her Ba ngoai. She was my grandmother, and this is the story of how she died.

To see my grandmother for the warrior woman that she was to me, you have to know that her farmer father sent her to the city for schooling because he didn't know what else to do with a daughter who was so smart. In early 20th‐century Vietnam, this was an unusual thing to do with a girl. She met my grandfather there, when he was a campus activist, helping him hand out nationalist leaflets. He introduced her to his Communist friends. After the French jailed my grandfather, my grandmother courted him by sending him long letters and care packages while he was in prison. When he was finally released, they married and started a family while he struggled financially as a newspaper publisher and at other odd jobs. But in 1947 his own Communist comrades killed him as part of a party purge. He had been forewarned, and opted to go quietly rather than try to escape because he was promised that this would guarantee her safety. Before they killed him, somewhere in the mountains, my grandfather gave a soldier friend a poem he wrote for his wife. When she told me this story 8 years ago, more than half a century later, she recited his love missive from memory.

At the time of her husband's death, my grandmother had 6 children, the last born just a few weeks before. After a few years of scraping by (she ran her own one‐room school for a while), she decided to leave Hanoi, and migrated south to Saigon with her brood. She was a famously strict parent, to hear my aunts and uncles tell it. She watched them like a hawk, worked full‐time, put them all through school, and eventually rose to a leadership position in the Ministry of Social Work in South Vietnam. My memories of Saigon life are punctuated by scenes of siblings and cousins running around at her regal house, yellow stucco with porticos and black iron gates, at a corner turn in the road, past a cemetery.

On this side of the world, to see her, you would never have thought that my grandmother had led such an epic life. She never worked again after immigrating with us in 1975. She lived on Social Security checks, gardened, said Buddhist prayers, and was nanny to her grandchildren. She watched soap operas religiously, and could report their full plot lines while sitting and knitting. She bundled her many sadnesses in a contented, 4‐foot 9‐inch frame.

Having no home of her own, she would move from one child's house to another every few months so as not to wear out her welcome. But her children lived in Pennsylvania, New Jersey, Florida, Texas, and Maryland. And in most of these cities, she had a different primary care physician. She has 21 grandchildren; 8 of us are physicians. Yet the aunts and uncles told us very little about her medical care. She preferred older‐generation Vietnamese physicians and I'm not sure that they were all competent, but her children did not want to argue with an octogenarian war survivor, and we deferred to their judgment. So we would find out only incidentally, for example, that a doctor prescribed her tuberculosis drugs for a visit to Vietnam.

For many years, Ba ngoai had no major medical problems. She was hypothyroid and hypertensive but on medication and generally high functioning. She had a lumpectomy for early‐stage breast cancer. Then, a year or so after she told me the story of my grandfather's death, Alzheimer's set in. It became harder for her to report symptoms reliably, and she became mildly depressed. Her grandchildren were now birthing our own babies, and we offered these as a distraction, trying to surround her with celebrations of these new fruits of her life labors.

Ba ngoai's decline worsened 3 years ago. She became more easily fatigued, depressed, and confused. A few months before she died, she started to get dyspneic, and couldn't go for short walks any more. In retrospect, I think that her prescription for thyroid replacement somehow fell through the cracks, probably in the transfer of care from one city to another, although there remains a great deal of confusion in the family about exactly what happened. Her thyroxine levels dwindled. One evening in October of that year, at my uncle's house in Maryland, she became severely short of breath and nearly unconscious. They called her Maryland PCP, who sent her to an emergency room at a local hospital. She was admitted in severe congestive heart failure. When the hospitalist spoke with my mother and uncle, he explained that he could take some fluid off her lungs, but that she might need to be intubated and admitted to intensive care. Looking back, I guessed that she probably needed pressors and invasive monitoring. He asked them, Is this what you want? My uncle said, No, it's not. And the hospitalist and the huddle of relatives decided she should come home.

The question was, into whose care would she be discharged? My elders were wary of contacting her PCP, partly because some blamed him for not catching and addressing her symptoms sooner, partly because to even confront him with this perception would cause him, and hence them, to lose face. This seemed too excruciating a scenario to them.

So at last, my uncle called my brother, the oldest grandchild and a very talented clinician. My brother is a pulmonologist, the kind of physician who once did a history and physical on a patient complaining only of Really feeling bad, Doc, and confidently started a steroid infusion before returning the next day with test results confirming his suspicion of Wegener's granulomatosis. He took my grandmother's physical by phone, and told my uncle to increase her furosemide dose. Then he said, I'm on call, but I'll be down there tomorrow. Call everyone together. Most of my relatives were already in town; they had come at news of her decline. She became alert enough for a couple of days to see and recognize most of the faces around her, like so many markers on a long journey. And then she died, slipped away.

I find it hard to define good coordination of care. My instinct as a researcher is to list measurable elements, but the tools we currently have generate metrics that are either reductionistsuch as how rapidly a physician returns a patient's callor so global that they no longer seem actionablesuch as patient satisfaction. But if such metrics set the goal in the distance, it seems useful to also define its counterpartdiscoordinationas a marker of the reality we would like to leave behind us as far as possible. Discoordination includes elements of discontinuity (lost patient history), fragmentation (actions by multiple players), overuse and/or inappropriate use of services, and ultimately, ineffective care (that is, the patient's needs go unmet).

My experience of discoordination was that of a Rube Goldberg contraption. It's composed of innumerable subtasks, each cleverly designed as the easiest solution to a seemingly short‐term problem, as quick fixes, but that in aggregate generate such chaos that the ultimate purpose is lost. They include acts of denial, lies to avoid embarrassment or conflict, and choices of convenience. My mother and her siblings accommodated my grandmother's choice of physicians by (secretly) not always adhering to care recommendations they didn't agree with, instead of challenging her. They took her to different physicians in different cities rather than risk embarrassing (due to an exaggerated sense of the smallness of the Vietnamese community) any one physician by dropping him. Her grandchildren, despite our medical training, found it culturally easier to defer to our elders than to intervene in substandard care. And none of her physicians aggressively followed up to ensure that a frail Alzheimer's patient was getting the care she needed. This is not to suggest that coordination is a simple task because Rube Goldberg machines make simple tasks complicated. Rather, it is a depiction of how indirectly we tend to address the problem.

I imagine a different course of events for my grandmother in the absence of discoordination. What if her children and physicians had understood and acknowledged to one another that her care was fragmented and therefore suboptimal? What if we grandchildren had confronted both Ba Ngoai and our parents sooner about their choice of physicians and offered to take on more of the burden of helping with her care decisions? Would we, as physicians, have been better able to ensure that her providers made rational clinical decisions? And what if she and her family had consistently recognized a single physician as her medical home? Snowbirding is hardly a rare phenomenon among Medicare patients; we could have designated one physician as primarily responsible for coordinating her care even without limiting her travel.

Care coordination is an inherently human activity. Supportive elements such as efficient transfer of medical information, resources for patient education and self‐care, and adequate reimbursement can take us to the brink of, but not actually bridge, the chasm that we want to cross. Traversing that divide sometimes requires settling turf issues over undesirable responsibilities between different physicians and between physicians and other providers; clarifying who has primary responsibility for different types of decisions (I lead on cardiac issues and her son leads on health maintenance); and the violation of cultural norms of patients, families, and/or providers. These can be uncomfortable, unpleasant conversations that at times seem beside the point. But in aggregate, they are the work of coordination, because they force us to align our expectations of one another. No level of information technology could have dismantled the Rube Goldberg machine that trapped my grandmother. Her last of many lessons for me was that emotional courage, honesty, and perseverance offer a much more direct path through the muck.

I was 12 years old before I knew her actual name, Le Thi Canh, because we always called her Ba ngoai. She was my grandmother, and this is the story of how she died.

To see my grandmother for the warrior woman that she was to me, you have to know that her farmer father sent her to the city for schooling because he didn't know what else to do with a daughter who was so smart. In early 20th‐century Vietnam, this was an unusual thing to do with a girl. She met my grandfather there, when he was a campus activist, helping him hand out nationalist leaflets. He introduced her to his Communist friends. After the French jailed my grandfather, my grandmother courted him by sending him long letters and care packages while he was in prison. When he was finally released, they married and started a family while he struggled financially as a newspaper publisher and at other odd jobs. But in 1947 his own Communist comrades killed him as part of a party purge. He had been forewarned, and opted to go quietly rather than try to escape because he was promised that this would guarantee her safety. Before they killed him, somewhere in the mountains, my grandfather gave a soldier friend a poem he wrote for his wife. When she told me this story 8 years ago, more than half a century later, she recited his love missive from memory.

At the time of her husband's death, my grandmother had 6 children, the last born just a few weeks before. After a few years of scraping by (she ran her own one‐room school for a while), she decided to leave Hanoi, and migrated south to Saigon with her brood. She was a famously strict parent, to hear my aunts and uncles tell it. She watched them like a hawk, worked full‐time, put them all through school, and eventually rose to a leadership position in the Ministry of Social Work in South Vietnam. My memories of Saigon life are punctuated by scenes of siblings and cousins running around at her regal house, yellow stucco with porticos and black iron gates, at a corner turn in the road, past a cemetery.

On this side of the world, to see her, you would never have thought that my grandmother had led such an epic life. She never worked again after immigrating with us in 1975. She lived on Social Security checks, gardened, said Buddhist prayers, and was nanny to her grandchildren. She watched soap operas religiously, and could report their full plot lines while sitting and knitting. She bundled her many sadnesses in a contented, 4‐foot 9‐inch frame.

Having no home of her own, she would move from one child's house to another every few months so as not to wear out her welcome. But her children lived in Pennsylvania, New Jersey, Florida, Texas, and Maryland. And in most of these cities, she had a different primary care physician. She has 21 grandchildren; 8 of us are physicians. Yet the aunts and uncles told us very little about her medical care. She preferred older‐generation Vietnamese physicians and I'm not sure that they were all competent, but her children did not want to argue with an octogenarian war survivor, and we deferred to their judgment. So we would find out only incidentally, for example, that a doctor prescribed her tuberculosis drugs for a visit to Vietnam.

For many years, Ba ngoai had no major medical problems. She was hypothyroid and hypertensive but on medication and generally high functioning. She had a lumpectomy for early‐stage breast cancer. Then, a year or so after she told me the story of my grandfather's death, Alzheimer's set in. It became harder for her to report symptoms reliably, and she became mildly depressed. Her grandchildren were now birthing our own babies, and we offered these as a distraction, trying to surround her with celebrations of these new fruits of her life labors.

Ba ngoai's decline worsened 3 years ago. She became more easily fatigued, depressed, and confused. A few months before she died, she started to get dyspneic, and couldn't go for short walks any more. In retrospect, I think that her prescription for thyroid replacement somehow fell through the cracks, probably in the transfer of care from one city to another, although there remains a great deal of confusion in the family about exactly what happened. Her thyroxine levels dwindled. One evening in October of that year, at my uncle's house in Maryland, she became severely short of breath and nearly unconscious. They called her Maryland PCP, who sent her to an emergency room at a local hospital. She was admitted in severe congestive heart failure. When the hospitalist spoke with my mother and uncle, he explained that he could take some fluid off her lungs, but that she might need to be intubated and admitted to intensive care. Looking back, I guessed that she probably needed pressors and invasive monitoring. He asked them, Is this what you want? My uncle said, No, it's not. And the hospitalist and the huddle of relatives decided she should come home.

The question was, into whose care would she be discharged? My elders were wary of contacting her PCP, partly because some blamed him for not catching and addressing her symptoms sooner, partly because to even confront him with this perception would cause him, and hence them, to lose face. This seemed too excruciating a scenario to them.

So at last, my uncle called my brother, the oldest grandchild and a very talented clinician. My brother is a pulmonologist, the kind of physician who once did a history and physical on a patient complaining only of Really feeling bad, Doc, and confidently started a steroid infusion before returning the next day with test results confirming his suspicion of Wegener's granulomatosis. He took my grandmother's physical by phone, and told my uncle to increase her furosemide dose. Then he said, I'm on call, but I'll be down there tomorrow. Call everyone together. Most of my relatives were already in town; they had come at news of her decline. She became alert enough for a couple of days to see and recognize most of the faces around her, like so many markers on a long journey. And then she died, slipped away.

I find it hard to define good coordination of care. My instinct as a researcher is to list measurable elements, but the tools we currently have generate metrics that are either reductionistsuch as how rapidly a physician returns a patient's callor so global that they no longer seem actionablesuch as patient satisfaction. But if such metrics set the goal in the distance, it seems useful to also define its counterpartdiscoordinationas a marker of the reality we would like to leave behind us as far as possible. Discoordination includes elements of discontinuity (lost patient history), fragmentation (actions by multiple players), overuse and/or inappropriate use of services, and ultimately, ineffective care (that is, the patient's needs go unmet).

My experience of discoordination was that of a Rube Goldberg contraption. It's composed of innumerable subtasks, each cleverly designed as the easiest solution to a seemingly short‐term problem, as quick fixes, but that in aggregate generate such chaos that the ultimate purpose is lost. They include acts of denial, lies to avoid embarrassment or conflict, and choices of convenience. My mother and her siblings accommodated my grandmother's choice of physicians by (secretly) not always adhering to care recommendations they didn't agree with, instead of challenging her. They took her to different physicians in different cities rather than risk embarrassing (due to an exaggerated sense of the smallness of the Vietnamese community) any one physician by dropping him. Her grandchildren, despite our medical training, found it culturally easier to defer to our elders than to intervene in substandard care. And none of her physicians aggressively followed up to ensure that a frail Alzheimer's patient was getting the care she needed. This is not to suggest that coordination is a simple task because Rube Goldberg machines make simple tasks complicated. Rather, it is a depiction of how indirectly we tend to address the problem.

I imagine a different course of events for my grandmother in the absence of discoordination. What if her children and physicians had understood and acknowledged to one another that her care was fragmented and therefore suboptimal? What if we grandchildren had confronted both Ba Ngoai and our parents sooner about their choice of physicians and offered to take on more of the burden of helping with her care decisions? Would we, as physicians, have been better able to ensure that her providers made rational clinical decisions? And what if she and her family had consistently recognized a single physician as her medical home? Snowbirding is hardly a rare phenomenon among Medicare patients; we could have designated one physician as primarily responsible for coordinating her care even without limiting her travel.

Care coordination is an inherently human activity. Supportive elements such as efficient transfer of medical information, resources for patient education and self‐care, and adequate reimbursement can take us to the brink of, but not actually bridge, the chasm that we want to cross. Traversing that divide sometimes requires settling turf issues over undesirable responsibilities between different physicians and between physicians and other providers; clarifying who has primary responsibility for different types of decisions (I lead on cardiac issues and her son leads on health maintenance); and the violation of cultural norms of patients, families, and/or providers. These can be uncomfortable, unpleasant conversations that at times seem beside the point. But in aggregate, they are the work of coordination, because they force us to align our expectations of one another. No level of information technology could have dismantled the Rube Goldberg machine that trapped my grandmother. Her last of many lessons for me was that emotional courage, honesty, and perseverance offer a much more direct path through the muck.

Hospitalists recognize the importance of the care transition from the inpatient setting to the outpatient setting, despite being described as causing a divorce between inpatient and outpatient care.1 If you do not believe this, just glance at the table of contents for this issue of the Journal of Hospital Medicine, which has 5 reports on research about various aspects of the hospital discharge transition complemented by an eloquent story of how a hospitalist facilitated the care coordination of one family's matriarch.2 An accompanying editorial proposes that hospitalists embrace the need of patients and their caregivers for care coordination.3 Thankfully, a growing number of academic hospitalists are focusing their efforts on identifying problems in the process and evaluating potential interventions to optimize it.

The hospital discharge process commonly has been an afterthought, concluding a typically intense experience for patients, some of whom may have begun the episode of hospitalization near death. After diagnostic evaluations and treatments, a patient has achieved stable enough status to be discharged home, and the inpatient physician has signed off with a simple may go in the written orders. The physician may feel absolved of responsibility as he expects the nurses to take care of instructions and to find transportation home for the patient. Unfortunately, this experience often is consistent with Webster's definition of discharge: to relieve of a charge, load, or burden unload release from an obligation. Some patients may feel like a Nolan Ryan fastball flying out of the hospital, but with no one to catch them.

Recognizing how the hospital discharge transition to home can be a perilous process fraught with failure,4 we laid out a research agenda for transitions of care. We are gratified to see the robust response from researchers published in this issue of the Journal of Hospital Medicine. The studies range from the description of a new tool to assess patients' mobility before discharge5 to evidence that the length of stay is prolonged (ie, delayed discharge) when the discharge diagnosis differs from that made on admission.6 Chen and colleagues analyzed the timing of discharge during the day and found that the duration of the discharge process was influenced by the need for consultation or a procedure prior to discharge; this finding is not surprising to practicing hospitalists. We agree with their conclusion that broad institutional efforts will be needed to facilitate the process. Hospitalists are part of a system and must engage the entire team to improve efficiency.

O'Leary and fellow hospitalists7 at Northwestern Memorial Hospital focused on creating a better discharge summary within their electronic health record with the aim of improved overall quality of the summaries and, just as important, timely completion. Despite some research indicating that absence of adequate communication between primary care providers and inpatient medical teams is not associated with adverse clinical outcomes,8 other research has demonstrated that it does affect outcomes and probably affects rehospitalization rates.9, 10 Moreover, another article in this issue describes a project undertaken at Baylor Health Care System (Dallas, TX) that demonstrated a reduction in emergency department visits and readmissions within 30 days post‐discharge among high‐risk elderly medical patients when a targeted care bundle was used.11 The results from this intervention, which consisted of medication counseling/reconciliation by a clinical pharmacist, condition‐specific enhanced discharge planning by a care coordinator, and phone follow‐up, confirm recent results from 2 similar studies.12, 13 These studies provide support for the idea that straightforward changes in the discharge process can improve patient outcomes.

Today in the United States, hospitalists likely care for the majority of hospitalized older patients.14 We strongly encourage them to use evidence‐based approaches to optimize the discharge process in their hospitals, and fortunately, clear guidance is available. Because of generous funding from the John A. Hartford Foundation, Project BOOST (Better Outcomes for Older Adults Through Safe Transitions) is mentoring 30 hospitals in an effort to implement the BOOST toolkit and improve their discharge transition processes.15 Another cost‐effective method involves the use of transition coaches to help the most vulnerable older patients with complex care needs.16 This approach is now being implemented by more than 100 healthcare organizations worldwide.17

Heartened by these exciting initiatives, we applaud the Society of Hospital Medicine's collaboration with the American College of Physicians, the Society of General Internal Medicine, the American Geriatrics Society, and the Society of Academic Emergency Medicine to produce a consensus policy statement on transitions of care that provides guiding principles for transitions both into and out of the hospital.18 Soon, all hospitalized patients and their caregivers may receive robust education prior to discharge, confirmation of their understanding with the teach‐back approach, medication reconciliation, and clear instructions for follow‐up, and the patient's primary care provider will be aware of all that has happened. Patients should expect nothing less than hospitalists ensuring their seamless transition from hospital to home.

Hospitalists recognize the importance of the care transition from the inpatient setting to the outpatient setting, despite being described as causing a divorce between inpatient and outpatient care.1 If you do not believe this, just glance at the table of contents for this issue of the Journal of Hospital Medicine, which has 5 reports on research about various aspects of the hospital discharge transition complemented by an eloquent story of how a hospitalist facilitated the care coordination of one family's matriarch.2 An accompanying editorial proposes that hospitalists embrace the need of patients and their caregivers for care coordination.3 Thankfully, a growing number of academic hospitalists are focusing their efforts on identifying problems in the process and evaluating potential interventions to optimize it.

The hospital discharge process commonly has been an afterthought, concluding a typically intense experience for patients, some of whom may have begun the episode of hospitalization near death. After diagnostic evaluations and treatments, a patient has achieved stable enough status to be discharged home, and the inpatient physician has signed off with a simple may go in the written orders. The physician may feel absolved of responsibility as he expects the nurses to take care of instructions and to find transportation home for the patient. Unfortunately, this experience often is consistent with Webster's definition of discharge: to relieve of a charge, load, or burden unload release from an obligation. Some patients may feel like a Nolan Ryan fastball flying out of the hospital, but with no one to catch them.

Recognizing how the hospital discharge transition to home can be a perilous process fraught with failure,4 we laid out a research agenda for transitions of care. We are gratified to see the robust response from researchers published in this issue of the Journal of Hospital Medicine. The studies range from the description of a new tool to assess patients' mobility before discharge5 to evidence that the length of stay is prolonged (ie, delayed discharge) when the discharge diagnosis differs from that made on admission.6 Chen and colleagues analyzed the timing of discharge during the day and found that the duration of the discharge process was influenced by the need for consultation or a procedure prior to discharge; this finding is not surprising to practicing hospitalists. We agree with their conclusion that broad institutional efforts will be needed to facilitate the process. Hospitalists are part of a system and must engage the entire team to improve efficiency.

O'Leary and fellow hospitalists7 at Northwestern Memorial Hospital focused on creating a better discharge summary within their electronic health record with the aim of improved overall quality of the summaries and, just as important, timely completion. Despite some research indicating that absence of adequate communication between primary care providers and inpatient medical teams is not associated with adverse clinical outcomes,8 other research has demonstrated that it does affect outcomes and probably affects rehospitalization rates.9, 10 Moreover, another article in this issue describes a project undertaken at Baylor Health Care System (Dallas, TX) that demonstrated a reduction in emergency department visits and readmissions within 30 days post‐discharge among high‐risk elderly medical patients when a targeted care bundle was used.11 The results from this intervention, which consisted of medication counseling/reconciliation by a clinical pharmacist, condition‐specific enhanced discharge planning by a care coordinator, and phone follow‐up, confirm recent results from 2 similar studies.12, 13 These studies provide support for the idea that straightforward changes in the discharge process can improve patient outcomes.

Today in the United States, hospitalists likely care for the majority of hospitalized older patients.14 We strongly encourage them to use evidence‐based approaches to optimize the discharge process in their hospitals, and fortunately, clear guidance is available. Because of generous funding from the John A. Hartford Foundation, Project BOOST (Better Outcomes for Older Adults Through Safe Transitions) is mentoring 30 hospitals in an effort to implement the BOOST toolkit and improve their discharge transition processes.15 Another cost‐effective method involves the use of transition coaches to help the most vulnerable older patients with complex care needs.16 This approach is now being implemented by more than 100 healthcare organizations worldwide.17

Heartened by these exciting initiatives, we applaud the Society of Hospital Medicine's collaboration with the American College of Physicians, the Society of General Internal Medicine, the American Geriatrics Society, and the Society of Academic Emergency Medicine to produce a consensus policy statement on transitions of care that provides guiding principles for transitions both into and out of the hospital.18 Soon, all hospitalized patients and their caregivers may receive robust education prior to discharge, confirmation of their understanding with the teach‐back approach, medication reconciliation, and clear instructions for follow‐up, and the patient's primary care provider will be aware of all that has happened. Patients should expect nothing less than hospitalists ensuring their seamless transition from hospital to home.

Hospitalists recognize the importance of the care transition from the inpatient setting to the outpatient setting, despite being described as causing a divorce between inpatient and outpatient care.1 If you do not believe this, just glance at the table of contents for this issue of the Journal of Hospital Medicine, which has 5 reports on research about various aspects of the hospital discharge transition complemented by an eloquent story of how a hospitalist facilitated the care coordination of one family's matriarch.2 An accompanying editorial proposes that hospitalists embrace the need of patients and their caregivers for care coordination.3 Thankfully, a growing number of academic hospitalists are focusing their efforts on identifying problems in the process and evaluating potential interventions to optimize it.

The hospital discharge process commonly has been an afterthought, concluding a typically intense experience for patients, some of whom may have begun the episode of hospitalization near death. After diagnostic evaluations and treatments, a patient has achieved stable enough status to be discharged home, and the inpatient physician has signed off with a simple may go in the written orders. The physician may feel absolved of responsibility as he expects the nurses to take care of instructions and to find transportation home for the patient. Unfortunately, this experience often is consistent with Webster's definition of discharge: to relieve of a charge, load, or burden unload release from an obligation. Some patients may feel like a Nolan Ryan fastball flying out of the hospital, but with no one to catch them.

Recognizing how the hospital discharge transition to home can be a perilous process fraught with failure,4 we laid out a research agenda for transitions of care. We are gratified to see the robust response from researchers published in this issue of the Journal of Hospital Medicine. The studies range from the description of a new tool to assess patients' mobility before discharge5 to evidence that the length of stay is prolonged (ie, delayed discharge) when the discharge diagnosis differs from that made on admission.6 Chen and colleagues analyzed the timing of discharge during the day and found that the duration of the discharge process was influenced by the need for consultation or a procedure prior to discharge; this finding is not surprising to practicing hospitalists. We agree with their conclusion that broad institutional efforts will be needed to facilitate the process. Hospitalists are part of a system and must engage the entire team to improve efficiency.

O'Leary and fellow hospitalists7 at Northwestern Memorial Hospital focused on creating a better discharge summary within their electronic health record with the aim of improved overall quality of the summaries and, just as important, timely completion. Despite some research indicating that absence of adequate communication between primary care providers and inpatient medical teams is not associated with adverse clinical outcomes,8 other research has demonstrated that it does affect outcomes and probably affects rehospitalization rates.9, 10 Moreover, another article in this issue describes a project undertaken at Baylor Health Care System (Dallas, TX) that demonstrated a reduction in emergency department visits and readmissions within 30 days post‐discharge among high‐risk elderly medical patients when a targeted care bundle was used.11 The results from this intervention, which consisted of medication counseling/reconciliation by a clinical pharmacist, condition‐specific enhanced discharge planning by a care coordinator, and phone follow‐up, confirm recent results from 2 similar studies.12, 13 These studies provide support for the idea that straightforward changes in the discharge process can improve patient outcomes.

Today in the United States, hospitalists likely care for the majority of hospitalized older patients.14 We strongly encourage them to use evidence‐based approaches to optimize the discharge process in their hospitals, and fortunately, clear guidance is available. Because of generous funding from the John A. Hartford Foundation, Project BOOST (Better Outcomes for Older Adults Through Safe Transitions) is mentoring 30 hospitals in an effort to implement the BOOST toolkit and improve their discharge transition processes.15 Another cost‐effective method involves the use of transition coaches to help the most vulnerable older patients with complex care needs.16 This approach is now being implemented by more than 100 healthcare organizations worldwide.17

Heartened by these exciting initiatives, we applaud the Society of Hospital Medicine's collaboration with the American College of Physicians, the Society of General Internal Medicine, the American Geriatrics Society, and the Society of Academic Emergency Medicine to produce a consensus policy statement on transitions of care that provides guiding principles for transitions both into and out of the hospital.18 Soon, all hospitalized patients and their caregivers may receive robust education prior to discharge, confirmation of their understanding with the teach‐back approach, medication reconciliation, and clear instructions for follow‐up, and the patient's primary care provider will be aware of all that has happened. Patients should expect nothing less than hospitalists ensuring their seamless transition from hospital to home.

Transient ischemic attacks (TIAs) are common and represent a clarion call to action to prevent disabling stroke. Incidence estimates for TIA range from 37 to 107 per 100,000 persons each year.1 Extrapolating from these data, there are likely greater than 100,000 to 300,000 TIAs in the US annually. Within 3 months, approximately 10% of these patients will suffer a stroke, with approximately one‐half of these events occurring within the first 48 hours after the sentinel TIA.26 Nearly two‐thirds of secondary strokes result in disability and 21% are fatal.3 Hospitalists are frequently called to provide care for patients with TIA and, as such, in order to establish an appropriate care plan, they require tools to better predict the likelihood and timing of a disabling stroke.7 In this review we examine the rationale for early aggressive TIA evaluation and treatment in the hospital, overview risk stratification models to identify the patients at highest risk for early recurrent ischemia, and explore application of these tools to admission policy and individualized patient care planning.

Definition

TIA is defined as a brief episode of neurological dysfunction caused by focal brain or retinal ischemia with clinical symptoms typically lasting less than 1 hour and without evidence of brain infarction.8, 9 Prior arbitrary time limits are being abandoned as advanced imaging techniques demonstrate that clinical examination lacks the sensitivity to detect small cerebral infarctions leading to misclassification of as many as 30% to 40% of strokes as TIAs.811 For cases in which imaging is not available, the diagnosis of clinically probable TIA is suggested. Patients with imaging consistent with stroke appear to be at 4‐fold to 10‐fold higher risk for subsequent ischemic events, thus the presence of subclinical infarcts may have clinical importance.2, 12 The majority of TIAs resolve within 1 hour of onset and neurologic deficit continuance beyond this time frame is more consistent with a stroke.13 Continuing symptoms after 1 hour mandates aggressive therapy in lieu of withholding intervention in the hopes of a spontaneous recovery.

Rationale for Hospitalization

Urgent evaluation and treatment within 24 to 48 hours of a TIA is recommended by the National Stroke Association (Table 1).14 These guidelines also recommend hospital admission for high‐risk patients. There are a number of compelling arguments for the hospitalization of a patient at high risk for subsequent stroke.

First, hospitalization offers potential for reduced time to thrombolysis for those patients who have a second ischemic event in the early period following TIA. Outpatients with new ischemic stroke may see hours pass between symptom onset and presentation to the emergency department (ED). This delay frequently places them outside of the thrombolytic window.1517 Hospitalization, assuming a well‐designed inpatient stroke care system, has great potential to reduce this delay. Approximately 50% of the stroke risk following a TIA is evident within 48 hours and rapid thrombolysis, available in an inpatient setting, is associated with improved outcome after stroke.3, 18 A cost‐utility analysis found that a 24‐hour admission for TIA patients to allow tissue plasminogen activator (t‐PA) for recurrent ischemia has a cost‐effectiveness ratio of $55,044 per quality‐adjusted life year with increasing cost effectiveness for the highest risk patients, such as those with a 24‐hour stroke risk of >5%.19

Second, hospital admission often facilitates the reliable and efficient evaluation for etiology and early initiation of secondary prevention. Neuroimaging, carotid ultrasound, echocardiography, and telemetry can be expedited with rapid initiation of proven secondary preventive therapies such as statin treatment, blood pressure control, and antithrombotic therapy. When indicated, carotid revascularization is recommended as soon as possible following TIA, with retrospective reviews suggesting improved outcomes when performed within 2 weeks of the event.1420 In one analysis, a negative association between hospitalization for TIA and subsequent stroke was discovered by review of Canadian population‐based administrative databases.5 While the mechanism for the negative association could not be established, the literature provides some support for hospitalization being associated with decreased risk for second strokes (hazard ratio [HR], 0.73; 95% confidence interval [CI], 0.570.95).5

Theoretically, much of this evaluation and treatment could occur in the outpatient setting but delays commonly seen in outpatient evaluation and the high potential for early second strokes for some patients may make this a risky care plan. Despite the high likelihood for serious outcomes following TIA and clear guidelines for early evaluation and management, current care often lacks a sense of urgency. A 2004 Canadian study revealed that three‐quarters of patients with a TIA were discharged directly from the ED with a resultant delay in diagnostic investigation.4 Over one‐third of patients were discharged without a prescription for antithrombotic therapy. American primary care practice patterns reveal even more significant delays in therapy, with only 2% of patients admitted to a hospital on the day of presentation for TIA, despite 80% of patients presenting for evaluation on the day of symptom onset.21 In this study less than one‐half of patients with atrial fibrillation were started on immediate anticoagulation.21 Further, as many as one‐third of patients did not have any evaluation in the month after the index event.21 Hospitalization for high‐risk patients has the potential to avoid these delays in outpatient evaluation and initiation of therapy.

Still, not all patients will require admission to a hospital setting. American EDs admit approximately one‐half of all TIAs, with regional variability not explained exclusively by clinical characteristics.22 Focusing on identifying the cohort of patients who would most benefit from hospitalization is paramount. In general, hospitalization should be reserved for patients with higher risk of an early secondary stroke. Specifically, admission is generally recommended for patients with crescendo symptoms, TIA on antithrombotic therapy, or symptoms lasting >1 hour.14 Additionally, patients with symptomatic carotid stenosis of 50% and presumed cardioembolic or hypercoagulable etiology merit hospital admission.14 In many cases these etiologies may not be known at time of presentation. Evaluation, such as carotid ultrasound, may not be readily available in the ED to inform the admission decision. Several new scoring systems that utilize routine clinical features available within an hour of presentation have been developed to more objectively assess the risk of secondary stroke following a TIA. The use of these prognostic scoring systems is recommended by the National Stroke Association to aid in triaging this cohort of patients.14

Prognostic Scoring Systems

New Models of Care: An Opportunity for Hospitalists

The key to improving TIA outcomes appears to be more contingent on the speed of evaluation and initiation of appropriate therapy than on the location of the care. The EXPRESS trial studied the effect of an immediate access neurovascular clinic providing urgent evaluation and immediate treatment of nonhospitalized TIA patients versus usual care. Statistically significant reductions were seen in time to evaluation, first treatment prescription, and in 90‐day risk of recurrent stroke (10.3% versus 2.1%, P < 0.0001) after the clinic was changed to the rapid evaluation and treatment model.33

The SOS‐TIA study used a 24‐hour access hospital‐based TIA clinic to evaluate the effects of rapid assessment and interventions on hospital length of stay and clinical outcomes.34 The 90‐day stroke rate was 1.24% (95% CI, 0.712.12), which represents a 79% reduction compared to the predicted stroke rate from the ABCD² scores. With expedited evaluation and treatment, 74% of patients were able to be sent home on the same day.

The results of these 2 new studies provide compelling evidence that rapid evaluation and treatment in the first 48 hours after TIA has the potential to alter outcomes. Unfortunately not all communities have access to same day TIA clinics. Still, these findings should embolden hospitalists to advocate for urgent evaluation, such as neurology and cardiac imaging and carotid evaluation, with immediate initiation of secondary preventive therapy and early surgical intervention when appropriate. In most cases these changes will require process transformations that present prime opportunities for hospitalists to reengineer systems of care.

Incorporating Prognostic Scores into Clinical Practice

Applying the evidence to practice requires calculation of the early risk but also awareness of the community resources available. High‐risk patients with an ABCD² score of 6 or 7 have a very high 8.1% risk of stroke within the next 48 hours. Given the catastrophic outcomes frequently seen after second strokes, these patients warrant inpatient admission to facilitate the immediate initiation of appropriate secondary prevention and potentially shorten time to thrombolysis if an early stroke occurs. Intermediate‐risk patients with ABCD² scores of 4 and 5 have a 4.1% 2‐day risk of stroke and may be considered for admission, hospital observation, or expedited clinic evaluation contingent on local availability. As many as one‐third of TIA patients will be categorized as low risk with a score of 0 to 3. These patients have a 2‐day risk of stroke of only 1% and are likely safe for prompt outpatient evaluation and management. The new, validated, ABCD² score is not a substitute for individualized judgment, but is helpful in developing admission guidelines in cooperation between neurologists, emergency room physicians, and hospitalists, and in using evidence‐based medicine to provide optimal care for the patient presenting with a TIA.

Stroke and TIA arise from identical etiologies, respond to the same secondary preventive measures, and should be considered part of the spectrum of an ischemic cerebral syndrome. Recognizing TIA as a medical emergency with high rates of secondary stroke and subsequent disability allows institution of therapies with appropriate urgency. Hospitalization offers the ability to rapidly coordinate the testing and secondary prevention measures but also, for high‐risk patients, offers the opportunity to reduce the time to thrombolysis for early recurrent strokes. New, validated scoring systems such as the ABCD² score help the hospitalist to decide which patients are appropriate for admission and which can be managed in progressive and traditional outpatient settings.

Transient ischemic attacks (TIAs) are common and represent a clarion call to action to prevent disabling stroke. Incidence estimates for TIA range from 37 to 107 per 100,000 persons each year.1 Extrapolating from these data, there are likely greater than 100,000 to 300,000 TIAs in the US annually. Within 3 months, approximately 10% of these patients will suffer a stroke, with approximately one‐half of these events occurring within the first 48 hours after the sentinel TIA.26 Nearly two‐thirds of secondary strokes result in disability and 21% are fatal.3 Hospitalists are frequently called to provide care for patients with TIA and, as such, in order to establish an appropriate care plan, they require tools to better predict the likelihood and timing of a disabling stroke.7 In this review we examine the rationale for early aggressive TIA evaluation and treatment in the hospital, overview risk stratification models to identify the patients at highest risk for early recurrent ischemia, and explore application of these tools to admission policy and individualized patient care planning.

Definition

TIA is defined as a brief episode of neurological dysfunction caused by focal brain or retinal ischemia with clinical symptoms typically lasting less than 1 hour and without evidence of brain infarction.8, 9 Prior arbitrary time limits are being abandoned as advanced imaging techniques demonstrate that clinical examination lacks the sensitivity to detect small cerebral infarctions leading to misclassification of as many as 30% to 40% of strokes as TIAs.811 For cases in which imaging is not available, the diagnosis of clinically probable TIA is suggested. Patients with imaging consistent with stroke appear to be at 4‐fold to 10‐fold higher risk for subsequent ischemic events, thus the presence of subclinical infarcts may have clinical importance.2, 12 The majority of TIAs resolve within 1 hour of onset and neurologic deficit continuance beyond this time frame is more consistent with a stroke.13 Continuing symptoms after 1 hour mandates aggressive therapy in lieu of withholding intervention in the hopes of a spontaneous recovery.

Rationale for Hospitalization

Urgent evaluation and treatment within 24 to 48 hours of a TIA is recommended by the National Stroke Association (Table 1).14 These guidelines also recommend hospital admission for high‐risk patients. There are a number of compelling arguments for the hospitalization of a patient at high risk for subsequent stroke.

First, hospitalization offers potential for reduced time to thrombolysis for those patients who have a second ischemic event in the early period following TIA. Outpatients with new ischemic stroke may see hours pass between symptom onset and presentation to the emergency department (ED). This delay frequently places them outside of the thrombolytic window.1517 Hospitalization, assuming a well‐designed inpatient stroke care system, has great potential to reduce this delay. Approximately 50% of the stroke risk following a TIA is evident within 48 hours and rapid thrombolysis, available in an inpatient setting, is associated with improved outcome after stroke.3, 18 A cost‐utility analysis found that a 24‐hour admission for TIA patients to allow tissue plasminogen activator (t‐PA) for recurrent ischemia has a cost‐effectiveness ratio of $55,044 per quality‐adjusted life year with increasing cost effectiveness for the highest risk patients, such as those with a 24‐hour stroke risk of >5%.19

Second, hospital admission often facilitates the reliable and efficient evaluation for etiology and early initiation of secondary prevention. Neuroimaging, carotid ultrasound, echocardiography, and telemetry can be expedited with rapid initiation of proven secondary preventive therapies such as statin treatment, blood pressure control, and antithrombotic therapy. When indicated, carotid revascularization is recommended as soon as possible following TIA, with retrospective reviews suggesting improved outcomes when performed within 2 weeks of the event.1420 In one analysis, a negative association between hospitalization for TIA and subsequent stroke was discovered by review of Canadian population‐based administrative databases.5 While the mechanism for the negative association could not be established, the literature provides some support for hospitalization being associated with decreased risk for second strokes (hazard ratio [HR], 0.73; 95% confidence interval [CI], 0.570.95).5

Theoretically, much of this evaluation and treatment could occur in the outpatient setting but delays commonly seen in outpatient evaluation and the high potential for early second strokes for some patients may make this a risky care plan. Despite the high likelihood for serious outcomes following TIA and clear guidelines for early evaluation and management, current care often lacks a sense of urgency. A 2004 Canadian study revealed that three‐quarters of patients with a TIA were discharged directly from the ED with a resultant delay in diagnostic investigation.4 Over one‐third of patients were discharged without a prescription for antithrombotic therapy. American primary care practice patterns reveal even more significant delays in therapy, with only 2% of patients admitted to a hospital on the day of presentation for TIA, despite 80% of patients presenting for evaluation on the day of symptom onset.21 In this study less than one‐half of patients with atrial fibrillation were started on immediate anticoagulation.21 Further, as many as one‐third of patients did not have any evaluation in the month after the index event.21 Hospitalization for high‐risk patients has the potential to avoid these delays in outpatient evaluation and initiation of therapy.

Still, not all patients will require admission to a hospital setting. American EDs admit approximately one‐half of all TIAs, with regional variability not explained exclusively by clinical characteristics.22 Focusing on identifying the cohort of patients who would most benefit from hospitalization is paramount. In general, hospitalization should be reserved for patients with higher risk of an early secondary stroke. Specifically, admission is generally recommended for patients with crescendo symptoms, TIA on antithrombotic therapy, or symptoms lasting >1 hour.14 Additionally, patients with symptomatic carotid stenosis of 50% and presumed cardioembolic or hypercoagulable etiology merit hospital admission.14 In many cases these etiologies may not be known at time of presentation. Evaluation, such as carotid ultrasound, may not be readily available in the ED to inform the admission decision. Several new scoring systems that utilize routine clinical features available within an hour of presentation have been developed to more objectively assess the risk of secondary stroke following a TIA. The use of these prognostic scoring systems is recommended by the National Stroke Association to aid in triaging this cohort of patients.14

Prognostic Scoring Systems

New Models of Care: An Opportunity for Hospitalists

The key to improving TIA outcomes appears to be more contingent on the speed of evaluation and initiation of appropriate therapy than on the location of the care. The EXPRESS trial studied the effect of an immediate access neurovascular clinic providing urgent evaluation and immediate treatment of nonhospitalized TIA patients versus usual care. Statistically significant reductions were seen in time to evaluation, first treatment prescription, and in 90‐day risk of recurrent stroke (10.3% versus 2.1%, P < 0.0001) after the clinic was changed to the rapid evaluation and treatment model.33

The SOS‐TIA study used a 24‐hour access hospital‐based TIA clinic to evaluate the effects of rapid assessment and interventions on hospital length of stay and clinical outcomes.34 The 90‐day stroke rate was 1.24% (95% CI, 0.712.12), which represents a 79% reduction compared to the predicted stroke rate from the ABCD² scores. With expedited evaluation and treatment, 74% of patients were able to be sent home on the same day.

The results of these 2 new studies provide compelling evidence that rapid evaluation and treatment in the first 48 hours after TIA has the potential to alter outcomes. Unfortunately not all communities have access to same day TIA clinics. Still, these findings should embolden hospitalists to advocate for urgent evaluation, such as neurology and cardiac imaging and carotid evaluation, with immediate initiation of secondary preventive therapy and early surgical intervention when appropriate. In most cases these changes will require process transformations that present prime opportunities for hospitalists to reengineer systems of care.

Incorporating Prognostic Scores into Clinical Practice

Applying the evidence to practice requires calculation of the early risk but also awareness of the community resources available. High‐risk patients with an ABCD² score of 6 or 7 have a very high 8.1% risk of stroke within the next 48 hours. Given the catastrophic outcomes frequently seen after second strokes, these patients warrant inpatient admission to facilitate the immediate initiation of appropriate secondary prevention and potentially shorten time to thrombolysis if an early stroke occurs. Intermediate‐risk patients with ABCD² scores of 4 and 5 have a 4.1% 2‐day risk of stroke and may be considered for admission, hospital observation, or expedited clinic evaluation contingent on local availability. As many as one‐third of TIA patients will be categorized as low risk with a score of 0 to 3. These patients have a 2‐day risk of stroke of only 1% and are likely safe for prompt outpatient evaluation and management. The new, validated, ABCD² score is not a substitute for individualized judgment, but is helpful in developing admission guidelines in cooperation between neurologists, emergency room physicians, and hospitalists, and in using evidence‐based medicine to provide optimal care for the patient presenting with a TIA.

Stroke and TIA arise from identical etiologies, respond to the same secondary preventive measures, and should be considered part of the spectrum of an ischemic cerebral syndrome. Recognizing TIA as a medical emergency with high rates of secondary stroke and subsequent disability allows institution of therapies with appropriate urgency. Hospitalization offers the ability to rapidly coordinate the testing and secondary prevention measures but also, for high‐risk patients, offers the opportunity to reduce the time to thrombolysis for early recurrent strokes. New, validated scoring systems such as the ABCD² score help the hospitalist to decide which patients are appropriate for admission and which can be managed in progressive and traditional outpatient settings.

Transient ischemic attacks (TIAs) are common and represent a clarion call to action to prevent disabling stroke. Incidence estimates for TIA range from 37 to 107 per 100,000 persons each year.1 Extrapolating from these data, there are likely greater than 100,000 to 300,000 TIAs in the US annually. Within 3 months, approximately 10% of these patients will suffer a stroke, with approximately one‐half of these events occurring within the first 48 hours after the sentinel TIA.26 Nearly two‐thirds of secondary strokes result in disability and 21% are fatal.3 Hospitalists are frequently called to provide care for patients with TIA and, as such, in order to establish an appropriate care plan, they require tools to better predict the likelihood and timing of a disabling stroke.7 In this review we examine the rationale for early aggressive TIA evaluation and treatment in the hospital, overview risk stratification models to identify the patients at highest risk for early recurrent ischemia, and explore application of these tools to admission policy and individualized patient care planning.

Definition

TIA is defined as a brief episode of neurological dysfunction caused by focal brain or retinal ischemia with clinical symptoms typically lasting less than 1 hour and without evidence of brain infarction.8, 9 Prior arbitrary time limits are being abandoned as advanced imaging techniques demonstrate that clinical examination lacks the sensitivity to detect small cerebral infarctions leading to misclassification of as many as 30% to 40% of strokes as TIAs.811 For cases in which imaging is not available, the diagnosis of clinically probable TIA is suggested. Patients with imaging consistent with stroke appear to be at 4‐fold to 10‐fold higher risk for subsequent ischemic events, thus the presence of subclinical infarcts may have clinical importance.2, 12 The majority of TIAs resolve within 1 hour of onset and neurologic deficit continuance beyond this time frame is more consistent with a stroke.13 Continuing symptoms after 1 hour mandates aggressive therapy in lieu of withholding intervention in the hopes of a spontaneous recovery.

Rationale for Hospitalization

Urgent evaluation and treatment within 24 to 48 hours of a TIA is recommended by the National Stroke Association (Table 1).14 These guidelines also recommend hospital admission for high‐risk patients. There are a number of compelling arguments for the hospitalization of a patient at high risk for subsequent stroke.

First, hospitalization offers potential for reduced time to thrombolysis for those patients who have a second ischemic event in the early period following TIA. Outpatients with new ischemic stroke may see hours pass between symptom onset and presentation to the emergency department (ED). This delay frequently places them outside of the thrombolytic window.1517 Hospitalization, assuming a well‐designed inpatient stroke care system, has great potential to reduce this delay. Approximately 50% of the stroke risk following a TIA is evident within 48 hours and rapid thrombolysis, available in an inpatient setting, is associated with improved outcome after stroke.3, 18 A cost‐utility analysis found that a 24‐hour admission for TIA patients to allow tissue plasminogen activator (t‐PA) for recurrent ischemia has a cost‐effectiveness ratio of $55,044 per quality‐adjusted life year with increasing cost effectiveness for the highest risk patients, such as those with a 24‐hour stroke risk of >5%.19

Second, hospital admission often facilitates the reliable and efficient evaluation for etiology and early initiation of secondary prevention. Neuroimaging, carotid ultrasound, echocardiography, and telemetry can be expedited with rapid initiation of proven secondary preventive therapies such as statin treatment, blood pressure control, and antithrombotic therapy. When indicated, carotid revascularization is recommended as soon as possible following TIA, with retrospective reviews suggesting improved outcomes when performed within 2 weeks of the event.1420 In one analysis, a negative association between hospitalization for TIA and subsequent stroke was discovered by review of Canadian population‐based administrative databases.5 While the mechanism for the negative association could not be established, the literature provides some support for hospitalization being associated with decreased risk for second strokes (hazard ratio [HR], 0.73; 95% confidence interval [CI], 0.570.95).5

Theoretically, much of this evaluation and treatment could occur in the outpatient setting but delays commonly seen in outpatient evaluation and the high potential for early second strokes for some patients may make this a risky care plan. Despite the high likelihood for serious outcomes following TIA and clear guidelines for early evaluation and management, current care often lacks a sense of urgency. A 2004 Canadian study revealed that three‐quarters of patients with a TIA were discharged directly from the ED with a resultant delay in diagnostic investigation.4 Over one‐third of patients were discharged without a prescription for antithrombotic therapy. American primary care practice patterns reveal even more significant delays in therapy, with only 2% of patients admitted to a hospital on the day of presentation for TIA, despite 80% of patients presenting for evaluation on the day of symptom onset.21 In this study less than one‐half of patients with atrial fibrillation were started on immediate anticoagulation.21 Further, as many as one‐third of patients did not have any evaluation in the month after the index event.21 Hospitalization for high‐risk patients has the potential to avoid these delays in outpatient evaluation and initiation of therapy.

Still, not all patients will require admission to a hospital setting. American EDs admit approximately one‐half of all TIAs, with regional variability not explained exclusively by clinical characteristics.22 Focusing on identifying the cohort of patients who would most benefit from hospitalization is paramount. In general, hospitalization should be reserved for patients with higher risk of an early secondary stroke. Specifically, admission is generally recommended for patients with crescendo symptoms, TIA on antithrombotic therapy, or symptoms lasting >1 hour.14 Additionally, patients with symptomatic carotid stenosis of 50% and presumed cardioembolic or hypercoagulable etiology merit hospital admission.14 In many cases these etiologies may not be known at time of presentation. Evaluation, such as carotid ultrasound, may not be readily available in the ED to inform the admission decision. Several new scoring systems that utilize routine clinical features available within an hour of presentation have been developed to more objectively assess the risk of secondary stroke following a TIA. The use of these prognostic scoring systems is recommended by the National Stroke Association to aid in triaging this cohort of patients.14

Prognostic Scoring Systems

New Models of Care: An Opportunity for Hospitalists

The key to improving TIA outcomes appears to be more contingent on the speed of evaluation and initiation of appropriate therapy than on the location of the care. The EXPRESS trial studied the effect of an immediate access neurovascular clinic providing urgent evaluation and immediate treatment of nonhospitalized TIA patients versus usual care. Statistically significant reductions were seen in time to evaluation, first treatment prescription, and in 90‐day risk of recurrent stroke (10.3% versus 2.1%, P < 0.0001) after the clinic was changed to the rapid evaluation and treatment model.33

The SOS‐TIA study used a 24‐hour access hospital‐based TIA clinic to evaluate the effects of rapid assessment and interventions on hospital length of stay and clinical outcomes.34 The 90‐day stroke rate was 1.24% (95% CI, 0.712.12), which represents a 79% reduction compared to the predicted stroke rate from the ABCD² scores. With expedited evaluation and treatment, 74% of patients were able to be sent home on the same day.

The results of these 2 new studies provide compelling evidence that rapid evaluation and treatment in the first 48 hours after TIA has the potential to alter outcomes. Unfortunately not all communities have access to same day TIA clinics. Still, these findings should embolden hospitalists to advocate for urgent evaluation, such as neurology and cardiac imaging and carotid evaluation, with immediate initiation of secondary preventive therapy and early surgical intervention when appropriate. In most cases these changes will require process transformations that present prime opportunities for hospitalists to reengineer systems of care.

Incorporating Prognostic Scores into Clinical Practice

Applying the evidence to practice requires calculation of the early risk but also awareness of the community resources available. High‐risk patients with an ABCD² score of 6 or 7 have a very high 8.1% risk of stroke within the next 48 hours. Given the catastrophic outcomes frequently seen after second strokes, these patients warrant inpatient admission to facilitate the immediate initiation of appropriate secondary prevention and potentially shorten time to thrombolysis if an early stroke occurs. Intermediate‐risk patients with ABCD² scores of 4 and 5 have a 4.1% 2‐day risk of stroke and may be considered for admission, hospital observation, or expedited clinic evaluation contingent on local availability. As many as one‐third of TIA patients will be categorized as low risk with a score of 0 to 3. These patients have a 2‐day risk of stroke of only 1% and are likely safe for prompt outpatient evaluation and management. The new, validated, ABCD² score is not a substitute for individualized judgment, but is helpful in developing admission guidelines in cooperation between neurologists, emergency room physicians, and hospitalists, and in using evidence‐based medicine to provide optimal care for the patient presenting with a TIA.

Stroke and TIA arise from identical etiologies, respond to the same secondary preventive measures, and should be considered part of the spectrum of an ischemic cerebral syndrome. Recognizing TIA as a medical emergency with high rates of secondary stroke and subsequent disability allows institution of therapies with appropriate urgency. Hospitalization offers the ability to rapidly coordinate the testing and secondary prevention measures but also, for high‐risk patients, offers the opportunity to reduce the time to thrombolysis for early recurrent strokes. New, validated scoring systems such as the ABCD² score help the hospitalist to decide which patients are appropriate for admission and which can be managed in progressive and traditional outpatient settings.

Recent research has found that the addition of clinical data to administrative data strengthens the accuracy of predicting inpatient mortality.1, 2 Pine et al.1 showed that including present on admission (POA) codes and numerical laboratory data resulted in substantially better fitting risk adjustment models than those based on administrative data alone. Risk adjustment models, despite improvement with the use of POA codes, are still imperfect and severity adjustment alone does not explain differences in mortality as well as we would hope.2

The addition of POA codes improves prediction of mortality, since they distinguish between conditions that were present at the time of admission and conditions that were acquired during the hospitalization, but it is not known if the addition of these codes is related to other measures of hospital performancesuch as differences in length of stay (LOS). Which of the factors related to the patient's clinical condition at the time of hospital admission drive differences in outcomes?

A patient's admission diagnosis may be an important piece of information that accounts for differences in hospital care. A patient's diagnosis at the time of hospital admission leads to the initial course of treatment. If the admitting diagnosis is inaccurate, a physician may spend critical time following a course of unneeded treatment until the correct diagnosis is made (reflected by a discrepancy between the admitting and discharge diagnosis codes). This discrepancy may be a marker of the fact that, while some patients are admitted to the hospital for treatment of a previously diagnosed condition, other patients require a diagnostic workup to determine the clinical problem.

A discrepancy may also reflect poor systems of documenting critical information and result in delays in care, with potentially serious health consequences.3, 4 If diagnosis discrepancy is a marker of difficult‐to‐diagnose cases, leading to delays in care, we may be able to improve our understanding of perceived differences in the production of high‐quality medical care and proactively identify cases which need more attention at admission to ensure that necessary care is provided as quickly as possible.

Almost universally, comparisons of hospital performance are risk‐adjusted to account for differences in case mix and severity across institutions. These risk‐adjustment models rely on discharge diagnoses to adjust for clinical differences among patients, even though recent research has shown that models using discharge diagnoses alone are inadequate predictors of variation in mortality among hospitals. While the findings of Pine et al.1 suggest the need to add certain clinical information, such as laboratory values, to improve these models, this information may be costly for some institutions to collect and report. We aimed to explore whether other simple to measure factors that are independent of the quality of care provided and routinely collected by hospitals' electronic information systems can be used to improve risk‐adjustment models. To assess the potential of other routinely collected diagnostic information in explaining differences in health outcomes, this study examined whether a discrepancy between the admission and discharge diagnoses was associated with hospital LOS.

Patients and Methods

Results

Of the 5,375 patients discharged between July 2005 and June 2006, 75.6% had a discrepancy between their admitting and principal discharge diagnosis. Patients with a discrepancy between their admitting and principal discharge diagnosis codes had significantly longer LOS, were older, had more comorbid conditions, and were more likely to be male, admitted through the ED, and have Medicare (Table 1). Results were similar for the more encompassing definition of a discrepancy between admitting and the top 5 discharge diagnoses (results not shown).

Table 2 reports the 10 most common admitting diagnoses that did not match the principal discharge diagnosis code and the 10 most common principal discharge diagnoses that did not match the admitting diagnosis code. The top 10 discrepant admitting diagnosis codes represented nearly one‐half of all cases with a discrepancy between the admitting and discharge diagnoses. The top 10 principal discharge diagnosis codes represented 23% of all discrepant diagnoses. Table 3 lists the 10 most common pairs of mismatched admitting and principal discharge diagnosis codes. The most common mismatched pair was a principal admitting diagnosis code of 786.05 (shortness of breath) and discharge diagnosis code of 428.0 (congestive heart failure, unspecified).

Table 4 reports the results of the generalized linear model predicting LOS. Discrepancy between the admitting and principal discharge diagnoses was associated with a 22.5% longer LOS (P < 0.01), translating into a 0.76‐day increase at the mean for those with discrepant diagnoses. Our results are robust to our definition of discrepancy between admitting and discharge diagnoses. Using the discrepancy definition based on the top five discharge diagnosis codes, a discrepancy between admitting and discharge diagnoses was associated with a 15.4% longer LOS (P < 0.01), translating into a 0.52‐day increase. Results of the likelihood ratio test showed that the addition of diagnosis discrepancy significantly improved the fit of the regression models using both the principal and top five discharge diagnosis codes.

Broadening our definition of a match between admitting and discharge diagnosis codes from matching only on the principal discharge diagnosis code to the first 10 discharge diagnosis codes showed that even when using the first 10 discharge diagnoses, a diagnosis discrepancy still significantly increased LOS. The magnitude weakened, however, as the definition of a match in diagnosis codes was broadened, ranging from 22.5% when including the principal discharge diagnosis code only to 12.1% when including the first 10 discharge diagnosis codes (Figure 1).

Figure 1

Discussion

Discrepancy between admitting and discharge diagnosis codes was associated with a large increase in LOS, even after controlling for age, sex, admission source, insurance, number of comorbid conditions, and clinical domain. This discrepancy translated into an increase of 0.76 days in LOS per general medicine patient, nearly two‐thirds larger than the increase in LOS of 0.47 days associated with having one comorbid condition, and equated to 4,102 additional patient days for the 5,375 general internal medicine patients admitted.

The relative and absolute increase in LOS associated with a diagnosis discrepancy is considerably larger than that associated with measures of comorbid illness found in other studies. In a study examining the predictive power of comorbidity measures based on diagnosis codes and outpatient pharmacy records, Parker et al.8 found that the inclusion of comorbid conditions based on only discharge diagnosis codes was associated with up to a 0.28‐day increase in LOS, and the further inclusion of comorbidity markers based on pharmacy data was associated with up to an additional 0.09‐day LOS. In a study comparing different measures of disease severity and comorbidities in predicting LOS for total knee replacement patients, Melfi et al.9 found that the addition of one diagnosis code was associated with a 3.3% increase in LOS. Similarly, Kieszak et al.10 found that the likelihood of having an LOS greater than 10 days increased two‐fold for patients with carotid endartectomy and at least one comorbidity.

While a discrepancy between the admitting and discharge diagnosis codes was consistently associated with an increased LOS, the underlying reasons are not yet understood. We can only speculate about the reasons for this association, and further work is needed to test these hypotheses. There are several possible explanations for discrepant cases: (1) poorer documentation at the time of admission, (2) more complexity in terms of the diagnostic task, and (3) less thorough diagnostic workup at the time of admission.

First, we do not think that poor documentation at the time of admission is the most likely explanation. Our ED uses documentation templates for all admitted patients, hence equalizing the amount of documentation for many patients. However, the main reason we do not think this is the reason for discrepancy is that diagnosis codes at the time of admission via the ED are assigned by physicians and not those who code based on documented information.

We do think that the most likely reason is that patients with discrepant diagnoses are truly harder to diagnose cases. For example, we assume that the time to provide care to patients once admitted is the same regardless of the ED or preadmission triage. For example, assume all patients are seen nearly as soon as admitted and the workup promptly ensues. Hence, under these conditions, variation in LOS may be due to more care needed for the most severely ill. If this assertion is true, our finding is a new one and adds a new candidate variable to explain variation in care due to patient severity (beyond comorbid illness, which we controlled for). We think we are showing that diagnostic uncertainty is a common, previously unexamined component of the complexity of clinical presentations (we propose that diagnosis discrepancy is a complexity variable rather than a comorbid, severity of illness variable). For example, discrepancy between admitting and discharge diagnosis codes could be due to other patient characteristics such as a patient's inability to communicate his or her symptoms to the physician due to language or cultural barriers.

However, regarding the third possible reason, if the ED or the preadmission setting fails to provide diagnostic services prior to admission for those patients with discrepant diagnoses regardless of diagnostic complexity, then our finding is a hospital or system performance variable. Those patients with discrepant diagnoses may have had a less thorough workup prior to admission leading to more workup being needed during the admission.

Regardless of the reason (perhaps all three reasons are involved at some level), our study points to a new component of patient care variations. We hope our finding spurs future research efforts. We are about to embark on a comparison of patients with identical discharge diagnoses but discrepant or not discrepant admission diagnoses to explore variations in the amount/type of diagnostic and treatment plans provided both before and during hospitalization.

In further support of diagnosis complexity as the reason for discrepancy is that the codes on admission for discrepantly coded patients are nonspecific, symptom or sign diagnoses (ie, shortness of breath, abdominal pain) while discharge diagnoses are more specific (ie, congestive heart failure, pancreatitis) (Tables 2 and 3). The nonspecific nature of the preliminary codes likely signifies more clinically complex situations and when noted, over and above previously described risk adjustment models, the discrepancy portends more healthcare needs. For patients admitted without a clear diagnosis of a clinical problem, diagnostic workups may be more complex and require longer hospitalization. For these patients, a longer LOS may not be a marker of poor quality of care, but instead the lack of critical information present at the time of admission.

Our comparison of the association between LOS and a discrepancy in diagnosis codes when the admitting diagnosis code was successively matched to a larger number of discharge diagnosis codes suggests that LOS increases not only when the admitting diagnosis is incorrect or not sufficiently specific, but also when the admitting diagnosis is correct, but not the principal discharge diagnosis. Taken together, these findings suggest that delays in care may result from lack of clear patient diagnostic information at the time of admission.

Our findings may advance the understanding of variations in hospital care from two standpoints. First, noting the discrepant diagnoses may significantly improve prediction in health services research studies examining variations in hospital performance, even beyond the addition of POA coding. Second, and perhaps more importantly, prospectively identifying patients at the time of admission with the nonspecific, preliminary codes identified in our study may allow physicians to target earlier in care patients with more demanding care needs. We realize, however, that before we could use this information to prospectively attempt to improve care, coding would have to be done at admission rather than discharge. At our site, this is true in the ED setting. Patients are assigned an admission diagnosis code as they leave the ED and this code is carried through to discharge without alteration. A nonspecific admission code could, for example, alert those taking care of the patient in the hospital that this is perhaps a more complex clinical situation requiring earlier consultation. Concurrent coding could also jumpstart studies to better understand whether what we have found in this preliminary study is due to poor assessment or difficult patient situations. However, this contingency may not be possible for those admitted directly from physician offices, as both the admission and discharge codes are determined at the time of discharge and based on documentation. Yet, on admission, a chief complaint is provided that may serve the same purpose as an admission diagnosis code if they are sufficiently in agreement.

Our study has limitations. It is from a single medical center and uses administrative data alone. We did not have access to clinical records for more detailed information about the content and completeness of medical records at the time of admission. Our observations should be tested in other hospital systems. Another limitation may be that we focus on discrepancy and not on those patients without a discrepancy. However, the aim of testing for discrepancy is to focus on improvement. Conducting a more in‐depth chart review of patients with similar final diagnoses, some with discrepant codes and others with nondiscrepant codes, may be a way to assess the reasons why LOS varied in the two groups. The next step, should our observations be confirmed, is to systematically assess whether other characteristics exist that differentiate cases in which a discrepancy between diagnosis codes is due to diagnostic uncertainty from those in which it is due to diagnostic oversight or error. A method to systematically identify conditions at admission that are likely to be misdiagnosed or have a delay in diagnosis may substantially improve the overall quality of care provided in the hospital.

Recent research has found that the addition of clinical data to administrative data strengthens the accuracy of predicting inpatient mortality.1, 2 Pine et al.1 showed that including present on admission (POA) codes and numerical laboratory data resulted in substantially better fitting risk adjustment models than those based on administrative data alone. Risk adjustment models, despite improvement with the use of POA codes, are still imperfect and severity adjustment alone does not explain differences in mortality as well as we would hope.2

The addition of POA codes improves prediction of mortality, since they distinguish between conditions that were present at the time of admission and conditions that were acquired during the hospitalization, but it is not known if the addition of these codes is related to other measures of hospital performancesuch as differences in length of stay (LOS). Which of the factors related to the patient's clinical condition at the time of hospital admission drive differences in outcomes?

A patient's admission diagnosis may be an important piece of information that accounts for differences in hospital care. A patient's diagnosis at the time of hospital admission leads to the initial course of treatment. If the admitting diagnosis is inaccurate, a physician may spend critical time following a course of unneeded treatment until the correct diagnosis is made (reflected by a discrepancy between the admitting and discharge diagnosis codes). This discrepancy may be a marker of the fact that, while some patients are admitted to the hospital for treatment of a previously diagnosed condition, other patients require a diagnostic workup to determine the clinical problem.

A discrepancy may also reflect poor systems of documenting critical information and result in delays in care, with potentially serious health consequences.3, 4 If diagnosis discrepancy is a marker of difficult‐to‐diagnose cases, leading to delays in care, we may be able to improve our understanding of perceived differences in the production of high‐quality medical care and proactively identify cases which need more attention at admission to ensure that necessary care is provided as quickly as possible.

Almost universally, comparisons of hospital performance are risk‐adjusted to account for differences in case mix and severity across institutions. These risk‐adjustment models rely on discharge diagnoses to adjust for clinical differences among patients, even though recent research has shown that models using discharge diagnoses alone are inadequate predictors of variation in mortality among hospitals. While the findings of Pine et al.1 suggest the need to add certain clinical information, such as laboratory values, to improve these models, this information may be costly for some institutions to collect and report. We aimed to explore whether other simple to measure factors that are independent of the quality of care provided and routinely collected by hospitals' electronic information systems can be used to improve risk‐adjustment models. To assess the potential of other routinely collected diagnostic information in explaining differences in health outcomes, this study examined whether a discrepancy between the admission and discharge diagnoses was associated with hospital LOS.

Patients and Methods

Results

Of the 5,375 patients discharged between July 2005 and June 2006, 75.6% had a discrepancy between their admitting and principal discharge diagnosis. Patients with a discrepancy between their admitting and principal discharge diagnosis codes had significantly longer LOS, were older, had more comorbid conditions, and were more likely to be male, admitted through the ED, and have Medicare (Table 1). Results were similar for the more encompassing definition of a discrepancy between admitting and the top 5 discharge diagnoses (results not shown).

Table 2 reports the 10 most common admitting diagnoses that did not match the principal discharge diagnosis code and the 10 most common principal discharge diagnoses that did not match the admitting diagnosis code. The top 10 discrepant admitting diagnosis codes represented nearly one‐half of all cases with a discrepancy between the admitting and discharge diagnoses. The top 10 principal discharge diagnosis codes represented 23% of all discrepant diagnoses. Table 3 lists the 10 most common pairs of mismatched admitting and principal discharge diagnosis codes. The most common mismatched pair was a principal admitting diagnosis code of 786.05 (shortness of breath) and discharge diagnosis code of 428.0 (congestive heart failure, unspecified).

Table 4 reports the results of the generalized linear model predicting LOS. Discrepancy between the admitting and principal discharge diagnoses was associated with a 22.5% longer LOS (P < 0.01), translating into a 0.76‐day increase at the mean for those with discrepant diagnoses. Our results are robust to our definition of discrepancy between admitting and discharge diagnoses. Using the discrepancy definition based on the top five discharge diagnosis codes, a discrepancy between admitting and discharge diagnoses was associated with a 15.4% longer LOS (P < 0.01), translating into a 0.52‐day increase. Results of the likelihood ratio test showed that the addition of diagnosis discrepancy significantly improved the fit of the regression models using both the principal and top five discharge diagnosis codes.

Broadening our definition of a match between admitting and discharge diagnosis codes from matching only on the principal discharge diagnosis code to the first 10 discharge diagnosis codes showed that even when using the first 10 discharge diagnoses, a diagnosis discrepancy still significantly increased LOS. The magnitude weakened, however, as the definition of a match in diagnosis codes was broadened, ranging from 22.5% when including the principal discharge diagnosis code only to 12.1% when including the first 10 discharge diagnosis codes (Figure 1).

Figure 1

Discussion

Discrepancy between admitting and discharge diagnosis codes was associated with a large increase in LOS, even after controlling for age, sex, admission source, insurance, number of comorbid conditions, and clinical domain. This discrepancy translated into an increase of 0.76 days in LOS per general medicine patient, nearly two‐thirds larger than the increase in LOS of 0.47 days associated with having one comorbid condition, and equated to 4,102 additional patient days for the 5,375 general internal medicine patients admitted.

The relative and absolute increase in LOS associated with a diagnosis discrepancy is considerably larger than that associated with measures of comorbid illness found in other studies. In a study examining the predictive power of comorbidity measures based on diagnosis codes and outpatient pharmacy records, Parker et al.8 found that the inclusion of comorbid conditions based on only discharge diagnosis codes was associated with up to a 0.28‐day increase in LOS, and the further inclusion of comorbidity markers based on pharmacy data was associated with up to an additional 0.09‐day LOS. In a study comparing different measures of disease severity and comorbidities in predicting LOS for total knee replacement patients, Melfi et al.9 found that the addition of one diagnosis code was associated with a 3.3% increase in LOS. Similarly, Kieszak et al.10 found that the likelihood of having an LOS greater than 10 days increased two‐fold for patients with carotid endartectomy and at least one comorbidity.

While a discrepancy between the admitting and discharge diagnosis codes was consistently associated with an increased LOS, the underlying reasons are not yet understood. We can only speculate about the reasons for this association, and further work is needed to test these hypotheses. There are several possible explanations for discrepant cases: (1) poorer documentation at the time of admission, (2) more complexity in terms of the diagnostic task, and (3) less thorough diagnostic workup at the time of admission.

First, we do not think that poor documentation at the time of admission is the most likely explanation. Our ED uses documentation templates for all admitted patients, hence equalizing the amount of documentation for many patients. However, the main reason we do not think this is the reason for discrepancy is that diagnosis codes at the time of admission via the ED are assigned by physicians and not those who code based on documented information.

We do think that the most likely reason is that patients with discrepant diagnoses are truly harder to diagnose cases. For example, we assume that the time to provide care to patients once admitted is the same regardless of the ED or preadmission triage. For example, assume all patients are seen nearly as soon as admitted and the workup promptly ensues. Hence, under these conditions, variation in LOS may be due to more care needed for the most severely ill. If this assertion is true, our finding is a new one and adds a new candidate variable to explain variation in care due to patient severity (beyond comorbid illness, which we controlled for). We think we are showing that diagnostic uncertainty is a common, previously unexamined component of the complexity of clinical presentations (we propose that diagnosis discrepancy is a complexity variable rather than a comorbid, severity of illness variable). For example, discrepancy between admitting and discharge diagnosis codes could be due to other patient characteristics such as a patient's inability to communicate his or her symptoms to the physician due to language or cultural barriers.

However, regarding the third possible reason, if the ED or the preadmission setting fails to provide diagnostic services prior to admission for those patients with discrepant diagnoses regardless of diagnostic complexity, then our finding is a hospital or system performance variable. Those patients with discrepant diagnoses may have had a less thorough workup prior to admission leading to more workup being needed during the admission.

Regardless of the reason (perhaps all three reasons are involved at some level), our study points to a new component of patient care variations. We hope our finding spurs future research efforts. We are about to embark on a comparison of patients with identical discharge diagnoses but discrepant or not discrepant admission diagnoses to explore variations in the amount/type of diagnostic and treatment plans provided both before and during hospitalization.

In further support of diagnosis complexity as the reason for discrepancy is that the codes on admission for discrepantly coded patients are nonspecific, symptom or sign diagnoses (ie, shortness of breath, abdominal pain) while discharge diagnoses are more specific (ie, congestive heart failure, pancreatitis) (Tables 2 and 3). The nonspecific nature of the preliminary codes likely signifies more clinically complex situations and when noted, over and above previously described risk adjustment models, the discrepancy portends more healthcare needs. For patients admitted without a clear diagnosis of a clinical problem, diagnostic workups may be more complex and require longer hospitalization. For these patients, a longer LOS may not be a marker of poor quality of care, but instead the lack of critical information present at the time of admission.

Our comparison of the association between LOS and a discrepancy in diagnosis codes when the admitting diagnosis code was successively matched to a larger number of discharge diagnosis codes suggests that LOS increases not only when the admitting diagnosis is incorrect or not sufficiently specific, but also when the admitting diagnosis is correct, but not the principal discharge diagnosis. Taken together, these findings suggest that delays in care may result from lack of clear patient diagnostic information at the time of admission.

Our findings may advance the understanding of variations in hospital care from two standpoints. First, noting the discrepant diagnoses may significantly improve prediction in health services research studies examining variations in hospital performance, even beyond the addition of POA coding. Second, and perhaps more importantly, prospectively identifying patients at the time of admission with the nonspecific, preliminary codes identified in our study may allow physicians to target earlier in care patients with more demanding care needs. We realize, however, that before we could use this information to prospectively attempt to improve care, coding would have to be done at admission rather than discharge. At our site, this is true in the ED setting. Patients are assigned an admission diagnosis code as they leave the ED and this code is carried through to discharge without alteration. A nonspecific admission code could, for example, alert those taking care of the patient in the hospital that this is perhaps a more complex clinical situation requiring earlier consultation. Concurrent coding could also jumpstart studies to better understand whether what we have found in this preliminary study is due to poor assessment or difficult patient situations. However, this contingency may not be possible for those admitted directly from physician offices, as both the admission and discharge codes are determined at the time of discharge and based on documentation. Yet, on admission, a chief complaint is provided that may serve the same purpose as an admission diagnosis code if they are sufficiently in agreement.

Our study has limitations. It is from a single medical center and uses administrative data alone. We did not have access to clinical records for more detailed information about the content and completeness of medical records at the time of admission. Our observations should be tested in other hospital systems. Another limitation may be that we focus on discrepancy and not on those patients without a discrepancy. However, the aim of testing for discrepancy is to focus on improvement. Conducting a more in‐depth chart review of patients with similar final diagnoses, some with discrepant codes and others with nondiscrepant codes, may be a way to assess the reasons why LOS varied in the two groups. The next step, should our observations be confirmed, is to systematically assess whether other characteristics exist that differentiate cases in which a discrepancy between diagnosis codes is due to diagnostic uncertainty from those in which it is due to diagnostic oversight or error. A method to systematically identify conditions at admission that are likely to be misdiagnosed or have a delay in diagnosis may substantially improve the overall quality of care provided in the hospital.

Recent research has found that the addition of clinical data to administrative data strengthens the accuracy of predicting inpatient mortality.1, 2 Pine et al.1 showed that including present on admission (POA) codes and numerical laboratory data resulted in substantially better fitting risk adjustment models than those based on administrative data alone. Risk adjustment models, despite improvement with the use of POA codes, are still imperfect and severity adjustment alone does not explain differences in mortality as well as we would hope.2

The addition of POA codes improves prediction of mortality, since they distinguish between conditions that were present at the time of admission and conditions that were acquired during the hospitalization, but it is not known if the addition of these codes is related to other measures of hospital performancesuch as differences in length of stay (LOS). Which of the factors related to the patient's clinical condition at the time of hospital admission drive differences in outcomes?

A patient's admission diagnosis may be an important piece of information that accounts for differences in hospital care. A patient's diagnosis at the time of hospital admission leads to the initial course of treatment. If the admitting diagnosis is inaccurate, a physician may spend critical time following a course of unneeded treatment until the correct diagnosis is made (reflected by a discrepancy between the admitting and discharge diagnosis codes). This discrepancy may be a marker of the fact that, while some patients are admitted to the hospital for treatment of a previously diagnosed condition, other patients require a diagnostic workup to determine the clinical problem.

A discrepancy may also reflect poor systems of documenting critical information and result in delays in care, with potentially serious health consequences.3, 4 If diagnosis discrepancy is a marker of difficult‐to‐diagnose cases, leading to delays in care, we may be able to improve our understanding of perceived differences in the production of high‐quality medical care and proactively identify cases which need more attention at admission to ensure that necessary care is provided as quickly as possible.

Almost universally, comparisons of hospital performance are risk‐adjusted to account for differences in case mix and severity across institutions. These risk‐adjustment models rely on discharge diagnoses to adjust for clinical differences among patients, even though recent research has shown that models using discharge diagnoses alone are inadequate predictors of variation in mortality among hospitals. While the findings of Pine et al.1 suggest the need to add certain clinical information, such as laboratory values, to improve these models, this information may be costly for some institutions to collect and report. We aimed to explore whether other simple to measure factors that are independent of the quality of care provided and routinely collected by hospitals' electronic information systems can be used to improve risk‐adjustment models. To assess the potential of other routinely collected diagnostic information in explaining differences in health outcomes, this study examined whether a discrepancy between the admission and discharge diagnoses was associated with hospital LOS.

Patients and Methods

Results

Of the 5,375 patients discharged between July 2005 and June 2006, 75.6% had a discrepancy between their admitting and principal discharge diagnosis. Patients with a discrepancy between their admitting and principal discharge diagnosis codes had significantly longer LOS, were older, had more comorbid conditions, and were more likely to be male, admitted through the ED, and have Medicare (Table 1). Results were similar for the more encompassing definition of a discrepancy between admitting and the top 5 discharge diagnoses (results not shown).

Table 2 reports the 10 most common admitting diagnoses that did not match the principal discharge diagnosis code and the 10 most common principal discharge diagnoses that did not match the admitting diagnosis code. The top 10 discrepant admitting diagnosis codes represented nearly one‐half of all cases with a discrepancy between the admitting and discharge diagnoses. The top 10 principal discharge diagnosis codes represented 23% of all discrepant diagnoses. Table 3 lists the 10 most common pairs of mismatched admitting and principal discharge diagnosis codes. The most common mismatched pair was a principal admitting diagnosis code of 786.05 (shortness of breath) and discharge diagnosis code of 428.0 (congestive heart failure, unspecified).

Table 4 reports the results of the generalized linear model predicting LOS. Discrepancy between the admitting and principal discharge diagnoses was associated with a 22.5% longer LOS (P < 0.01), translating into a 0.76‐day increase at the mean for those with discrepant diagnoses. Our results are robust to our definition of discrepancy between admitting and discharge diagnoses. Using the discrepancy definition based on the top five discharge diagnosis codes, a discrepancy between admitting and discharge diagnoses was associated with a 15.4% longer LOS (P < 0.01), translating into a 0.52‐day increase. Results of the likelihood ratio test showed that the addition of diagnosis discrepancy significantly improved the fit of the regression models using both the principal and top five discharge diagnosis codes.

Broadening our definition of a match between admitting and discharge diagnosis codes from matching only on the principal discharge diagnosis code to the first 10 discharge diagnosis codes showed that even when using the first 10 discharge diagnoses, a diagnosis discrepancy still significantly increased LOS. The magnitude weakened, however, as the definition of a match in diagnosis codes was broadened, ranging from 22.5% when including the principal discharge diagnosis code only to 12.1% when including the first 10 discharge diagnosis codes (Figure 1).

Figure 1

Discussion

Discrepancy between admitting and discharge diagnosis codes was associated with a large increase in LOS, even after controlling for age, sex, admission source, insurance, number of comorbid conditions, and clinical domain. This discrepancy translated into an increase of 0.76 days in LOS per general medicine patient, nearly two‐thirds larger than the increase in LOS of 0.47 days associated with having one comorbid condition, and equated to 4,102 additional patient days for the 5,375 general internal medicine patients admitted.

The relative and absolute increase in LOS associated with a diagnosis discrepancy is considerably larger than that associated with measures of comorbid illness found in other studies. In a study examining the predictive power of comorbidity measures based on diagnosis codes and outpatient pharmacy records, Parker et al.8 found that the inclusion of comorbid conditions based on only discharge diagnosis codes was associated with up to a 0.28‐day increase in LOS, and the further inclusion of comorbidity markers based on pharmacy data was associated with up to an additional 0.09‐day LOS. In a study comparing different measures of disease severity and comorbidities in predicting LOS for total knee replacement patients, Melfi et al.9 found that the addition of one diagnosis code was associated with a 3.3% increase in LOS. Similarly, Kieszak et al.10 found that the likelihood of having an LOS greater than 10 days increased two‐fold for patients with carotid endartectomy and at least one comorbidity.

While a discrepancy between the admitting and discharge diagnosis codes was consistently associated with an increased LOS, the underlying reasons are not yet understood. We can only speculate about the reasons for this association, and further work is needed to test these hypotheses. There are several possible explanations for discrepant cases: (1) poorer documentation at the time of admission, (2) more complexity in terms of the diagnostic task, and (3) less thorough diagnostic workup at the time of admission.

First, we do not think that poor documentation at the time of admission is the most likely explanation. Our ED uses documentation templates for all admitted patients, hence equalizing the amount of documentation for many patients. However, the main reason we do not think this is the reason for discrepancy is that diagnosis codes at the time of admission via the ED are assigned by physicians and not those who code based on documented information.

We do think that the most likely reason is that patients with discrepant diagnoses are truly harder to diagnose cases. For example, we assume that the time to provide care to patients once admitted is the same regardless of the ED or preadmission triage. For example, assume all patients are seen nearly as soon as admitted and the workup promptly ensues. Hence, under these conditions, variation in LOS may be due to more care needed for the most severely ill. If this assertion is true, our finding is a new one and adds a new candidate variable to explain variation in care due to patient severity (beyond comorbid illness, which we controlled for). We think we are showing that diagnostic uncertainty is a common, previously unexamined component of the complexity of clinical presentations (we propose that diagnosis discrepancy is a complexity variable rather than a comorbid, severity of illness variable). For example, discrepancy between admitting and discharge diagnosis codes could be due to other patient characteristics such as a patient's inability to communicate his or her symptoms to the physician due to language or cultural barriers.

However, regarding the third possible reason, if the ED or the preadmission setting fails to provide diagnostic services prior to admission for those patients with discrepant diagnoses regardless of diagnostic complexity, then our finding is a hospital or system performance variable. Those patients with discrepant diagnoses may have had a less thorough workup prior to admission leading to more workup being needed during the admission.

Regardless of the reason (perhaps all three reasons are involved at some level), our study points to a new component of patient care variations. We hope our finding spurs future research efforts. We are about to embark on a comparison of patients with identical discharge diagnoses but discrepant or not discrepant admission diagnoses to explore variations in the amount/type of diagnostic and treatment plans provided both before and during hospitalization.

In further support of diagnosis complexity as the reason for discrepancy is that the codes on admission for discrepantly coded patients are nonspecific, symptom or sign diagnoses (ie, shortness of breath, abdominal pain) while discharge diagnoses are more specific (ie, congestive heart failure, pancreatitis) (Tables 2 and 3). The nonspecific nature of the preliminary codes likely signifies more clinically complex situations and when noted, over and above previously described risk adjustment models, the discrepancy portends more healthcare needs. For patients admitted without a clear diagnosis of a clinical problem, diagnostic workups may be more complex and require longer hospitalization. For these patients, a longer LOS may not be a marker of poor quality of care, but instead the lack of critical information present at the time of admission.

Our comparison of the association between LOS and a discrepancy in diagnosis codes when the admitting diagnosis code was successively matched to a larger number of discharge diagnosis codes suggests that LOS increases not only when the admitting diagnosis is incorrect or not sufficiently specific, but also when the admitting diagnosis is correct, but not the principal discharge diagnosis. Taken together, these findings suggest that delays in care may result from lack of clear patient diagnostic information at the time of admission.

Our findings may advance the understanding of variations in hospital care from two standpoints. First, noting the discrepant diagnoses may significantly improve prediction in health services research studies examining variations in hospital performance, even beyond the addition of POA coding. Second, and perhaps more importantly, prospectively identifying patients at the time of admission with the nonspecific, preliminary codes identified in our study may allow physicians to target earlier in care patients with more demanding care needs. We realize, however, that before we could use this information to prospectively attempt to improve care, coding would have to be done at admission rather than discharge. At our site, this is true in the ED setting. Patients are assigned an admission diagnosis code as they leave the ED and this code is carried through to discharge without alteration. A nonspecific admission code could, for example, alert those taking care of the patient in the hospital that this is perhaps a more complex clinical situation requiring earlier consultation. Concurrent coding could also jumpstart studies to better understand whether what we have found in this preliminary study is due to poor assessment or difficult patient situations. However, this contingency may not be possible for those admitted directly from physician offices, as both the admission and discharge codes are determined at the time of discharge and based on documentation. Yet, on admission, a chief complaint is provided that may serve the same purpose as an admission diagnosis code if they are sufficiently in agreement.

Our study has limitations. It is from a single medical center and uses administrative data alone. We did not have access to clinical records for more detailed information about the content and completeness of medical records at the time of admission. Our observations should be tested in other hospital systems. Another limitation may be that we focus on discrepancy and not on those patients without a discrepancy. However, the aim of testing for discrepancy is to focus on improvement. Conducting a more in‐depth chart review of patients with similar final diagnoses, some with discrepant codes and others with nondiscrepant codes, may be a way to assess the reasons why LOS varied in the two groups. The next step, should our observations be confirmed, is to systematically assess whether other characteristics exist that differentiate cases in which a discrepancy between diagnosis codes is due to diagnostic uncertainty from those in which it is due to diagnostic oversight or error. A method to systematically identify conditions at admission that are likely to be misdiagnosed or have a delay in diagnosis may substantially improve the overall quality of care provided in the hospital.

Hospitalized patients are often debilitated, either from their admitting illness or from the deconditioning that occurs with inactivity. Functional decline, which appears to progress in a hierarchical pattern,1 occurs in 24% to 50% of geriatric patients during hospitalization and is poorly documented.2 Such a decline is associated not only with longer hospital stays and increased health care costs but also with higher mortality.3 The American College of Physicians, through its Assessing Care of Vulnerable Elders project, expressly endorsed gait and mobility evaluation as a quality indicator, and examination insufficiency is well documented.4

Of the several existing mobility assessment tools, few are used routinely in hospital. Some require complex scoring; others require timing and/or a trained occupational therapist.5 We created a simplified tool named Independent Mobility Validation Examination (I‐MOVE) for use by bedside caregivers. We evaluated the tool's face validity and interobserver agreement.

I‐MOVE

I‐MOVE, represented schematically in Figure 1, is a performance test that assesses the patient's ability to perform a sequence of 6 basic tasks: rolling over in bed, sitting up, standing, transferring to a chair, walking in the room, and walking in the hallway. Most motor functions can be assumed to be hierarchical in nature; any patient who can perform at the highest level, such as walking safely, also would be expected to perform at the lowest level.

Figure 1

Instructions for administering I‐MOVE are as follows:

• 1

  Review current orders. Exclude patients ordered on bed rest or non‐weight‐bearing or other orders precluding any of the 6 requested actions.

• 2

  Prepare environment.
• a

  Chair at bedside.

• b

  Lower side bed rail closest to chair.

• c

  Clear path for patient to ambulate.

• d

  Ensure patient dons slippers.

• e

  Flatten bed.

• f

  Ensure any gait assistive device, if generally used by the patient, is within reach from the bedside.

• 3

  Requests for patient action (for steps c through f, make available and within reach any appropriate gait‐assistance device such as walker or cane, if such is customarily used at home or newly prescribed):
• a

  With patient lying supine in bed, with close supervision, ask patient to turn from side to side in bed (request when both bed rails are up).

• b

  Lower side rail closer to chair and ask the patient to rise up to a sitting position and turn to sit up with legs dangling off the bed.

• c

  Ask the patient to stand.

• d

  Ask the patient to take a seat in the chair next to the bed.

• e

  Ask the patient to ambulate in the room.

• f

  Ask the patient to ambulate in the hallway.

• 4

  At any point if the patient seems incapable, unsteady, or unsafe to accomplish the requested task, render hands‐on assistance and immediately end the test.

• 5

  Document, by number (1‐12), the activity level successfully accomplished independently by the patient (even number levels) or accomplished with assistance (odd number levels).

• 6

  Patient may be considered independent if able to perform the activity with a normal assistive device (cane, walker, brace, or crutches) but not using furniture.

• 7

  Assistance is defined as any physical contact with the patient.

Findings

Face Validity

We sent surveys to 6 experienced practicing clinicians at our hospital: a geriatrician, a physiatrist, an exercise physiologist, an occupational therapist, a physical therapist, and a registered nurse. We asked each clinician to rate the 6 I‐MOVE elements (requested actions) for clinical relevance to mobility independence. Relevance of each element was measured on an ordinal scale with scores ranging from 1 to 4, with: 1 not relevant; 2 somewhat relevant; 3 quite relevant; and 4 very relevant. From the 5 responses we received, 4 evaluators ranked all 6 I‐MOVE requested actions as very relevant. The fifth evaluator ranked 5 of the 6 actions as very relevant and 1 action (walking in the room) as quite relevant. These results demonstrate general agreement that I‐MOVE is, at face value, a reasonable measure of independent mobility.

Interrater Reliability

The protocol was approved by the hospital's institutional review board. On a general medical unita non‐electrocardiographic telemetry, nonsurgical unit of an acute care hospital, where patients are assigned the primary service of an internal medicine physicianwe instructed 2 registered nurse (RN) volunteers (RN1 and RN2) in the I‐MOVE protocol. Each RN administered I‐MOVE independently to 41 consecutive, cognitively intact patients in a blinded fashion (ie, neither nurse was aware of the other's scoring of each patient) and within 1 hour of each other's assessment.

After administering I‐MOVE to each patient, the nurse judged and scored the patient's performance using the 12‐level I‐MOVE ordinal scale, ranging from a low value of 1, complete dependence, to the highest value of 12, complete independence. The patients' I‐MOVE score pairs recorded by RN1 and RN2 were statistically compared. Interrater reliability, a comparison of the 41 patients' score pairs, is graphically represented in Figure 2. The calculated intraclass correlation coefficient (r) was 0.90, indicating excellent agreement (r > 0.75).

Figure 2

Discussion

Traditional physical examinations by physicians and assessments by nurses do not routinely extend to standardized mobility testing and may fail to recognize disability. Of the existing mobility assessment tools, we believe that most are not suited to patients hospitalized on general medical units. I‐MOVE has been designed to address this need, with an emphasis on practicality and brevity to allow repetition at appropriate intervals (tracking), as is done for vital signs. In this initial study, I‐MOVE was found to have face‐valid content and excellent interrater agreement.

Our study had several limitations. Only 1 pair of test administrators was involved; the sample population was chosen by convenience; clustering of outcomes occurred at level 12, which may have augmented the agreement; and the study was limited to cognitively intact patients. Note that we chose to use the intraclass correlation coefficient rather than the statistic because the weighting between the ordinal I‐MOVE scores has not yet been studied and defined. Also, the weighted is asymptotically equivalent to the intraclass correlation coefficient.

I‐MOVE is intended to aid caregivers in the recognition of debility so that appropriate interventions such as physical therapy may be prescribed. It was designed to complement, not replace, specialized evaluations such as those performed by physical therapists, occupational therapists, or comprehensive geriatric assessments. This practical assessment of basic functioning may enhance communication among caregivers, patients, and patients' family members, especially with regard to discharge planning. Further study is needed to validate I‐MOVE against existing tools, evaluate I‐MOVE's utility as a vital sign, and discern whether a sharp or unexpected decline portends a medical complication.

Hospitalized patients are often debilitated, either from their admitting illness or from the deconditioning that occurs with inactivity. Functional decline, which appears to progress in a hierarchical pattern,1 occurs in 24% to 50% of geriatric patients during hospitalization and is poorly documented.2 Such a decline is associated not only with longer hospital stays and increased health care costs but also with higher mortality.3 The American College of Physicians, through its Assessing Care of Vulnerable Elders project, expressly endorsed gait and mobility evaluation as a quality indicator, and examination insufficiency is well documented.4

Of the several existing mobility assessment tools, few are used routinely in hospital. Some require complex scoring; others require timing and/or a trained occupational therapist.5 We created a simplified tool named Independent Mobility Validation Examination (I‐MOVE) for use by bedside caregivers. We evaluated the tool's face validity and interobserver agreement.

I‐MOVE

I‐MOVE, represented schematically in Figure 1, is a performance test that assesses the patient's ability to perform a sequence of 6 basic tasks: rolling over in bed, sitting up, standing, transferring to a chair, walking in the room, and walking in the hallway. Most motor functions can be assumed to be hierarchical in nature; any patient who can perform at the highest level, such as walking safely, also would be expected to perform at the lowest level.

Figure 1

Instructions for administering I‐MOVE are as follows:

• 1

  Review current orders. Exclude patients ordered on bed rest or non‐weight‐bearing or other orders precluding any of the 6 requested actions.

• 2

  Prepare environment.
• a

  Chair at bedside.

• b

  Lower side bed rail closest to chair.

• c

  Clear path for patient to ambulate.

• d

  Ensure patient dons slippers.

• e

  Flatten bed.

• f

  Ensure any gait assistive device, if generally used by the patient, is within reach from the bedside.

• 3

  Requests for patient action (for steps c through f, make available and within reach any appropriate gait‐assistance device such as walker or cane, if such is customarily used at home or newly prescribed):
• a

  With patient lying supine in bed, with close supervision, ask patient to turn from side to side in bed (request when both bed rails are up).

• b

  Lower side rail closer to chair and ask the patient to rise up to a sitting position and turn to sit up with legs dangling off the bed.

• c

  Ask the patient to stand.

• d

  Ask the patient to take a seat in the chair next to the bed.

• e

  Ask the patient to ambulate in the room.

• f

  Ask the patient to ambulate in the hallway.

• 4

  At any point if the patient seems incapable, unsteady, or unsafe to accomplish the requested task, render hands‐on assistance and immediately end the test.

• 5

  Document, by number (1‐12), the activity level successfully accomplished independently by the patient (even number levels) or accomplished with assistance (odd number levels).

• 6

  Patient may be considered independent if able to perform the activity with a normal assistive device (cane, walker, brace, or crutches) but not using furniture.

• 7

  Assistance is defined as any physical contact with the patient.

Findings

Face Validity

We sent surveys to 6 experienced practicing clinicians at our hospital: a geriatrician, a physiatrist, an exercise physiologist, an occupational therapist, a physical therapist, and a registered nurse. We asked each clinician to rate the 6 I‐MOVE elements (requested actions) for clinical relevance to mobility independence. Relevance of each element was measured on an ordinal scale with scores ranging from 1 to 4, with: 1 not relevant; 2 somewhat relevant; 3 quite relevant; and 4 very relevant. From the 5 responses we received, 4 evaluators ranked all 6 I‐MOVE requested actions as very relevant. The fifth evaluator ranked 5 of the 6 actions as very relevant and 1 action (walking in the room) as quite relevant. These results demonstrate general agreement that I‐MOVE is, at face value, a reasonable measure of independent mobility.

Interrater Reliability

The protocol was approved by the hospital's institutional review board. On a general medical unita non‐electrocardiographic telemetry, nonsurgical unit of an acute care hospital, where patients are assigned the primary service of an internal medicine physicianwe instructed 2 registered nurse (RN) volunteers (RN1 and RN2) in the I‐MOVE protocol. Each RN administered I‐MOVE independently to 41 consecutive, cognitively intact patients in a blinded fashion (ie, neither nurse was aware of the other's scoring of each patient) and within 1 hour of each other's assessment.

After administering I‐MOVE to each patient, the nurse judged and scored the patient's performance using the 12‐level I‐MOVE ordinal scale, ranging from a low value of 1, complete dependence, to the highest value of 12, complete independence. The patients' I‐MOVE score pairs recorded by RN1 and RN2 were statistically compared. Interrater reliability, a comparison of the 41 patients' score pairs, is graphically represented in Figure 2. The calculated intraclass correlation coefficient (r) was 0.90, indicating excellent agreement (r > 0.75).

Figure 2

Discussion

Traditional physical examinations by physicians and assessments by nurses do not routinely extend to standardized mobility testing and may fail to recognize disability. Of the existing mobility assessment tools, we believe that most are not suited to patients hospitalized on general medical units. I‐MOVE has been designed to address this need, with an emphasis on practicality and brevity to allow repetition at appropriate intervals (tracking), as is done for vital signs. In this initial study, I‐MOVE was found to have face‐valid content and excellent interrater agreement.

Our study had several limitations. Only 1 pair of test administrators was involved; the sample population was chosen by convenience; clustering of outcomes occurred at level 12, which may have augmented the agreement; and the study was limited to cognitively intact patients. Note that we chose to use the intraclass correlation coefficient rather than the statistic because the weighting between the ordinal I‐MOVE scores has not yet been studied and defined. Also, the weighted is asymptotically equivalent to the intraclass correlation coefficient.

I‐MOVE is intended to aid caregivers in the recognition of debility so that appropriate interventions such as physical therapy may be prescribed. It was designed to complement, not replace, specialized evaluations such as those performed by physical therapists, occupational therapists, or comprehensive geriatric assessments. This practical assessment of basic functioning may enhance communication among caregivers, patients, and patients' family members, especially with regard to discharge planning. Further study is needed to validate I‐MOVE against existing tools, evaluate I‐MOVE's utility as a vital sign, and discern whether a sharp or unexpected decline portends a medical complication.

Hospitalized patients are often debilitated, either from their admitting illness or from the deconditioning that occurs with inactivity. Functional decline, which appears to progress in a hierarchical pattern,1 occurs in 24% to 50% of geriatric patients during hospitalization and is poorly documented.2 Such a decline is associated not only with longer hospital stays and increased health care costs but also with higher mortality.3 The American College of Physicians, through its Assessing Care of Vulnerable Elders project, expressly endorsed gait and mobility evaluation as a quality indicator, and examination insufficiency is well documented.4

Of the several existing mobility assessment tools, few are used routinely in hospital. Some require complex scoring; others require timing and/or a trained occupational therapist.5 We created a simplified tool named Independent Mobility Validation Examination (I‐MOVE) for use by bedside caregivers. We evaluated the tool's face validity and interobserver agreement.

I‐MOVE

I‐MOVE, represented schematically in Figure 1, is a performance test that assesses the patient's ability to perform a sequence of 6 basic tasks: rolling over in bed, sitting up, standing, transferring to a chair, walking in the room, and walking in the hallway. Most motor functions can be assumed to be hierarchical in nature; any patient who can perform at the highest level, such as walking safely, also would be expected to perform at the lowest level.

Figure 1

Instructions for administering I‐MOVE are as follows:

• 1

  Review current orders. Exclude patients ordered on bed rest or non‐weight‐bearing or other orders precluding any of the 6 requested actions.

• 2

  Prepare environment.
• a

  Chair at bedside.

• b

  Lower side bed rail closest to chair.

• c

  Clear path for patient to ambulate.

• d

  Ensure patient dons slippers.

• e

  Flatten bed.

• f

  Ensure any gait assistive device, if generally used by the patient, is within reach from the bedside.

• 3

  Requests for patient action (for steps c through f, make available and within reach any appropriate gait‐assistance device such as walker or cane, if such is customarily used at home or newly prescribed):
• a

  With patient lying supine in bed, with close supervision, ask patient to turn from side to side in bed (request when both bed rails are up).

• b

  Lower side rail closer to chair and ask the patient to rise up to a sitting position and turn to sit up with legs dangling off the bed.

• c

  Ask the patient to stand.

• d

  Ask the patient to take a seat in the chair next to the bed.

• e

  Ask the patient to ambulate in the room.

• f

  Ask the patient to ambulate in the hallway.

• 4

  At any point if the patient seems incapable, unsteady, or unsafe to accomplish the requested task, render hands‐on assistance and immediately end the test.

• 5

  Document, by number (1‐12), the activity level successfully accomplished independently by the patient (even number levels) or accomplished with assistance (odd number levels).

• 6

  Patient may be considered independent if able to perform the activity with a normal assistive device (cane, walker, brace, or crutches) but not using furniture.

• 7

  Assistance is defined as any physical contact with the patient.

Findings

Face Validity

We sent surveys to 6 experienced practicing clinicians at our hospital: a geriatrician, a physiatrist, an exercise physiologist, an occupational therapist, a physical therapist, and a registered nurse. We asked each clinician to rate the 6 I‐MOVE elements (requested actions) for clinical relevance to mobility independence. Relevance of each element was measured on an ordinal scale with scores ranging from 1 to 4, with: 1 not relevant; 2 somewhat relevant; 3 quite relevant; and 4 very relevant. From the 5 responses we received, 4 evaluators ranked all 6 I‐MOVE requested actions as very relevant. The fifth evaluator ranked 5 of the 6 actions as very relevant and 1 action (walking in the room) as quite relevant. These results demonstrate general agreement that I‐MOVE is, at face value, a reasonable measure of independent mobility.

Interrater Reliability

The protocol was approved by the hospital's institutional review board. On a general medical unita non‐electrocardiographic telemetry, nonsurgical unit of an acute care hospital, where patients are assigned the primary service of an internal medicine physicianwe instructed 2 registered nurse (RN) volunteers (RN1 and RN2) in the I‐MOVE protocol. Each RN administered I‐MOVE independently to 41 consecutive, cognitively intact patients in a blinded fashion (ie, neither nurse was aware of the other's scoring of each patient) and within 1 hour of each other's assessment.

After administering I‐MOVE to each patient, the nurse judged and scored the patient's performance using the 12‐level I‐MOVE ordinal scale, ranging from a low value of 1, complete dependence, to the highest value of 12, complete independence. The patients' I‐MOVE score pairs recorded by RN1 and RN2 were statistically compared. Interrater reliability, a comparison of the 41 patients' score pairs, is graphically represented in Figure 2. The calculated intraclass correlation coefficient (r) was 0.90, indicating excellent agreement (r > 0.75).

Figure 2

Discussion

Traditional physical examinations by physicians and assessments by nurses do not routinely extend to standardized mobility testing and may fail to recognize disability. Of the existing mobility assessment tools, we believe that most are not suited to patients hospitalized on general medical units. I‐MOVE has been designed to address this need, with an emphasis on practicality and brevity to allow repetition at appropriate intervals (tracking), as is done for vital signs. In this initial study, I‐MOVE was found to have face‐valid content and excellent interrater agreement.

Our study had several limitations. Only 1 pair of test administrators was involved; the sample population was chosen by convenience; clustering of outcomes occurred at level 12, which may have augmented the agreement; and the study was limited to cognitively intact patients. Note that we chose to use the intraclass correlation coefficient rather than the statistic because the weighting between the ordinal I‐MOVE scores has not yet been studied and defined. Also, the weighted is asymptotically equivalent to the intraclass correlation coefficient.

I‐MOVE is intended to aid caregivers in the recognition of debility so that appropriate interventions such as physical therapy may be prescribed. It was designed to complement, not replace, specialized evaluations such as those performed by physical therapists, occupational therapists, or comprehensive geriatric assessments. This practical assessment of basic functioning may enhance communication among caregivers, patients, and patients' family members, especially with regard to discharge planning. Further study is needed to validate I‐MOVE against existing tools, evaluate I‐MOVE's utility as a vital sign, and discern whether a sharp or unexpected decline portends a medical complication.

The hospitalist is not typically the hero in contemporary narratives about medical practice. More often, the hospitalist is portrayed as an interloper, a doctor who works for the hospital and not the patient, an employee focused on efficiency and rapid discharge rather than continuous medical care. Elsewhere in this issue, Mai Pham1 offers an updated story in which a hospitalist organizes the loose ends of a patient's medical history and contributes significantly to healthcare coordination.

Hospitalists acknowledge that an admission to the hospital disrupts established outpatient continuity and that discharge can be a perilous event, with potential for medical errors. The Society of Hospital Medicine has recognized discontinuity as enough of a concern that care transitions are considered a core competency for hospital physicians.2 This competency requires hospitalists to be able to move a patient safely from the outpatient setting through the hospital wards and back home again.

As our specialty approaches two decades of practice experience, the work that we do in coordinating medical care and ensuring continuity has evolved and deepened. Initial efforts to coordinate care from the inpatient setting focused on how key hospital events could be best communicated to the patient's primary physician.3, 4 Communication at admission and at critical junctures was encouraged, and research demonstrated that a timely discharge summary sent to the primary care office could decrease hospital readmission.5

Experienced hospitalists recognize, however, that not every inpatient can identify a primary care doctor; sometimes, it is this very lack of established outpatient care that triggers a patient's admission. Reasons for discontinuous prehospital care include disrupted outpatient relationships, particularly as provider networks and insurance status are re‐evaluated, as well as cultural and social barriers. Complex, overcrowded outpatient health systems can be challenging to navigate even for the savviest of patients.

These concerns have helped us to focus on the hospital as a critical setting for delivering continuity of care. The mechanisms for ensuring continuity include, harnessing the inpatient capability for real‐time diagnosis and treatment synthesis, which, in Mai Pham's case,1 enabled decision‐making and timely care coordination for her dying grandmother. Hospitals typically offer an array of tools needed to assist physicians in coordinating a patient's care, including rapid diagnostic testing and simultaneous multidisciplinary evaluation with consulting physicians; nurses; case managers; physical, occupational, and speech therapists; pharmacists; nutritionists; social workers; and palliative care teams. The patient's family members and friends are frequently present in the inpatient setting and can provide additional data points that are not always available in a timely manner in the ambulatory setting. Each of these inpatient interactions can help patients to develop routes of access to healthcare after they are discharged from the hospital.

Despite the advantages of the hospital setting, however, the knock on hospitalists is that we are just on the clock. Frequent handoffs, both when physician shifts change and when a fresh hospitalist rotates on service, present a significant concern to seamless care.6 Increasing fragmentation in hospital staffing may correlate with lengthened hospital stay and increased difficulty in receiving follow‐up outpatient care.7 A new narrative for hospitalists, one focused on enhancing continuity, requires mindfulness toward schedule fragmentation and balances personal desires with the need to maintain a continued presence and availability for patients.

Enhancing continuity and care coordination in the hospital also means continually working to improve provider‐to‐provider communications. Solutions may include well‐executed chart documentation, with active concerns flagged for the oncoming physician, and an electronic medical record that is easy to access from various locations. Computerized templates may enable more thorough handoffs in certain settings.8 As the use of systems and checklists gains traction for their ability to reduce iatrogenic complications and save money,9 hospitalists may come to rely more widely on systems that improve continuity, especially for aspects of inpatient care such as medication reconciliation.10

We believe that the most critical way in which hospitalists can ensure continuous care involves increasing physician efforts to engage with patients during their hospitalization. Hospitalists meet patients at particularly intense and vulnerable times of life, and we have all observed how patients can lose autonomy simply by being hospitalized. In the hospital, things happen to patients, sometimes because of the sheer size and force of the inpatient team and the momentum of a hospital stay.

Yet hospitalists can quickly develop a rapport with their patients through the number and intensity of their patient interactions. The free‐form structure of the inpatient schedule means a flexibility to be present with patients on short notice, to respond to acute events in real time, and to be available to talk with family members and other caregivers at their convenience. Hospitalists can take part in multiple bedside interactions in a single day and on consecutive days. Because of this flexibility, hospitalists can bond with their patients in a short time frame11 as they access critical social and clinical contexts, often more efficiently than possible elsewhere. As one primary care physician wrote when she gave up caring for her hospitalized patients, I know what happened to my patient, but I didn't really experience it with my patient.12 Hospitalists do get to share in this drama.

The medical community has been slow to recognize that hospitalists, as much as any generalist physician, can and do engage patients actively in their medical care. The hospital can be an ideal setting to ensure continuity through real‐time diagnostics and therapeutics and even more so through the intense bonding that can happen between physicians and patients on the wards. The old story of an outpatient provider single‐handedly managing a patient's care is rapidly disappearing in many locales. However, the story of the hospitalist is more than that of the hero in waiting. The story is a cautionary tale, one in which the relationship between the hospitalist and his or her patients is still under development, a tale for which much work remains. As hospitalists, we must continue to refine our skills and systems to deliver continuous care for patients in transition. We must also continue to focus on experiences with our patients and their families and, when called upon, to engage in those challenging conversations that Mai Pham1 says force us to align our expectations of one another. Forging this human connection will always be part of seamless healthcare for every physician, not least for the hospitalist.

The hospitalist is not typically the hero in contemporary narratives about medical practice. More often, the hospitalist is portrayed as an interloper, a doctor who works for the hospital and not the patient, an employee focused on efficiency and rapid discharge rather than continuous medical care. Elsewhere in this issue, Mai Pham1 offers an updated story in which a hospitalist organizes the loose ends of a patient's medical history and contributes significantly to healthcare coordination.

Hospitalists acknowledge that an admission to the hospital disrupts established outpatient continuity and that discharge can be a perilous event, with potential for medical errors. The Society of Hospital Medicine has recognized discontinuity as enough of a concern that care transitions are considered a core competency for hospital physicians.2 This competency requires hospitalists to be able to move a patient safely from the outpatient setting through the hospital wards and back home again.

As our specialty approaches two decades of practice experience, the work that we do in coordinating medical care and ensuring continuity has evolved and deepened. Initial efforts to coordinate care from the inpatient setting focused on how key hospital events could be best communicated to the patient's primary physician.3, 4 Communication at admission and at critical junctures was encouraged, and research demonstrated that a timely discharge summary sent to the primary care office could decrease hospital readmission.5

Experienced hospitalists recognize, however, that not every inpatient can identify a primary care doctor; sometimes, it is this very lack of established outpatient care that triggers a patient's admission. Reasons for discontinuous prehospital care include disrupted outpatient relationships, particularly as provider networks and insurance status are re‐evaluated, as well as cultural and social barriers. Complex, overcrowded outpatient health systems can be challenging to navigate even for the savviest of patients.

These concerns have helped us to focus on the hospital as a critical setting for delivering continuity of care. The mechanisms for ensuring continuity include, harnessing the inpatient capability for real‐time diagnosis and treatment synthesis, which, in Mai Pham's case,1 enabled decision‐making and timely care coordination for her dying grandmother. Hospitals typically offer an array of tools needed to assist physicians in coordinating a patient's care, including rapid diagnostic testing and simultaneous multidisciplinary evaluation with consulting physicians; nurses; case managers; physical, occupational, and speech therapists; pharmacists; nutritionists; social workers; and palliative care teams. The patient's family members and friends are frequently present in the inpatient setting and can provide additional data points that are not always available in a timely manner in the ambulatory setting. Each of these inpatient interactions can help patients to develop routes of access to healthcare after they are discharged from the hospital.

Despite the advantages of the hospital setting, however, the knock on hospitalists is that we are just on the clock. Frequent handoffs, both when physician shifts change and when a fresh hospitalist rotates on service, present a significant concern to seamless care.6 Increasing fragmentation in hospital staffing may correlate with lengthened hospital stay and increased difficulty in receiving follow‐up outpatient care.7 A new narrative for hospitalists, one focused on enhancing continuity, requires mindfulness toward schedule fragmentation and balances personal desires with the need to maintain a continued presence and availability for patients.

Enhancing continuity and care coordination in the hospital also means continually working to improve provider‐to‐provider communications. Solutions may include well‐executed chart documentation, with active concerns flagged for the oncoming physician, and an electronic medical record that is easy to access from various locations. Computerized templates may enable more thorough handoffs in certain settings.8 As the use of systems and checklists gains traction for their ability to reduce iatrogenic complications and save money,9 hospitalists may come to rely more widely on systems that improve continuity, especially for aspects of inpatient care such as medication reconciliation.10

We believe that the most critical way in which hospitalists can ensure continuous care involves increasing physician efforts to engage with patients during their hospitalization. Hospitalists meet patients at particularly intense and vulnerable times of life, and we have all observed how patients can lose autonomy simply by being hospitalized. In the hospital, things happen to patients, sometimes because of the sheer size and force of the inpatient team and the momentum of a hospital stay.

Yet hospitalists can quickly develop a rapport with their patients through the number and intensity of their patient interactions. The free‐form structure of the inpatient schedule means a flexibility to be present with patients on short notice, to respond to acute events in real time, and to be available to talk with family members and other caregivers at their convenience. Hospitalists can take part in multiple bedside interactions in a single day and on consecutive days. Because of this flexibility, hospitalists can bond with their patients in a short time frame11 as they access critical social and clinical contexts, often more efficiently than possible elsewhere. As one primary care physician wrote when she gave up caring for her hospitalized patients, I know what happened to my patient, but I didn't really experience it with my patient.12 Hospitalists do get to share in this drama.

The medical community has been slow to recognize that hospitalists, as much as any generalist physician, can and do engage patients actively in their medical care. The hospital can be an ideal setting to ensure continuity through real‐time diagnostics and therapeutics and even more so through the intense bonding that can happen between physicians and patients on the wards. The old story of an outpatient provider single‐handedly managing a patient's care is rapidly disappearing in many locales. However, the story of the hospitalist is more than that of the hero in waiting. The story is a cautionary tale, one in which the relationship between the hospitalist and his or her patients is still under development, a tale for which much work remains. As hospitalists, we must continue to refine our skills and systems to deliver continuous care for patients in transition. We must also continue to focus on experiences with our patients and their families and, when called upon, to engage in those challenging conversations that Mai Pham1 says force us to align our expectations of one another. Forging this human connection will always be part of seamless healthcare for every physician, not least for the hospitalist.

The hospitalist is not typically the hero in contemporary narratives about medical practice. More often, the hospitalist is portrayed as an interloper, a doctor who works for the hospital and not the patient, an employee focused on efficiency and rapid discharge rather than continuous medical care. Elsewhere in this issue, Mai Pham1 offers an updated story in which a hospitalist organizes the loose ends of a patient's medical history and contributes significantly to healthcare coordination.

Hospitalists acknowledge that an admission to the hospital disrupts established outpatient continuity and that discharge can be a perilous event, with potential for medical errors. The Society of Hospital Medicine has recognized discontinuity as enough of a concern that care transitions are considered a core competency for hospital physicians.2 This competency requires hospitalists to be able to move a patient safely from the outpatient setting through the hospital wards and back home again.

As our specialty approaches two decades of practice experience, the work that we do in coordinating medical care and ensuring continuity has evolved and deepened. Initial efforts to coordinate care from the inpatient setting focused on how key hospital events could be best communicated to the patient's primary physician.3, 4 Communication at admission and at critical junctures was encouraged, and research demonstrated that a timely discharge summary sent to the primary care office could decrease hospital readmission.5

Experienced hospitalists recognize, however, that not every inpatient can identify a primary care doctor; sometimes, it is this very lack of established outpatient care that triggers a patient's admission. Reasons for discontinuous prehospital care include disrupted outpatient relationships, particularly as provider networks and insurance status are re‐evaluated, as well as cultural and social barriers. Complex, overcrowded outpatient health systems can be challenging to navigate even for the savviest of patients.

These concerns have helped us to focus on the hospital as a critical setting for delivering continuity of care. The mechanisms for ensuring continuity include, harnessing the inpatient capability for real‐time diagnosis and treatment synthesis, which, in Mai Pham's case,1 enabled decision‐making and timely care coordination for her dying grandmother. Hospitals typically offer an array of tools needed to assist physicians in coordinating a patient's care, including rapid diagnostic testing and simultaneous multidisciplinary evaluation with consulting physicians; nurses; case managers; physical, occupational, and speech therapists; pharmacists; nutritionists; social workers; and palliative care teams. The patient's family members and friends are frequently present in the inpatient setting and can provide additional data points that are not always available in a timely manner in the ambulatory setting. Each of these inpatient interactions can help patients to develop routes of access to healthcare after they are discharged from the hospital.

Despite the advantages of the hospital setting, however, the knock on hospitalists is that we are just on the clock. Frequent handoffs, both when physician shifts change and when a fresh hospitalist rotates on service, present a significant concern to seamless care.6 Increasing fragmentation in hospital staffing may correlate with lengthened hospital stay and increased difficulty in receiving follow‐up outpatient care.7 A new narrative for hospitalists, one focused on enhancing continuity, requires mindfulness toward schedule fragmentation and balances personal desires with the need to maintain a continued presence and availability for patients.

Enhancing continuity and care coordination in the hospital also means continually working to improve provider‐to‐provider communications. Solutions may include well‐executed chart documentation, with active concerns flagged for the oncoming physician, and an electronic medical record that is easy to access from various locations. Computerized templates may enable more thorough handoffs in certain settings.8 As the use of systems and checklists gains traction for their ability to reduce iatrogenic complications and save money,9 hospitalists may come to rely more widely on systems that improve continuity, especially for aspects of inpatient care such as medication reconciliation.10

We believe that the most critical way in which hospitalists can ensure continuous care involves increasing physician efforts to engage with patients during their hospitalization. Hospitalists meet patients at particularly intense and vulnerable times of life, and we have all observed how patients can lose autonomy simply by being hospitalized. In the hospital, things happen to patients, sometimes because of the sheer size and force of the inpatient team and the momentum of a hospital stay.

Yet hospitalists can quickly develop a rapport with their patients through the number and intensity of their patient interactions. The free‐form structure of the inpatient schedule means a flexibility to be present with patients on short notice, to respond to acute events in real time, and to be available to talk with family members and other caregivers at their convenience. Hospitalists can take part in multiple bedside interactions in a single day and on consecutive days. Because of this flexibility, hospitalists can bond with their patients in a short time frame11 as they access critical social and clinical contexts, often more efficiently than possible elsewhere. As one primary care physician wrote when she gave up caring for her hospitalized patients, I know what happened to my patient, but I didn't really experience it with my patient.12 Hospitalists do get to share in this drama.

The medical community has been slow to recognize that hospitalists, as much as any generalist physician, can and do engage patients actively in their medical care. The hospital can be an ideal setting to ensure continuity through real‐time diagnostics and therapeutics and even more so through the intense bonding that can happen between physicians and patients on the wards. The old story of an outpatient provider single‐handedly managing a patient's care is rapidly disappearing in many locales. However, the story of the hospitalist is more than that of the hero in waiting. The story is a cautionary tale, one in which the relationship between the hospitalist and his or her patients is still under development, a tale for which much work remains. As hospitalists, we must continue to refine our skills and systems to deliver continuous care for patients in transition. We must also continue to focus on experiences with our patients and their families and, when called upon, to engage in those challenging conversations that Mai Pham1 says force us to align our expectations of one another. Forging this human connection will always be part of seamless healthcare for every physician, not least for the hospitalist.

Elderly patients (aged 65 years and older) consume a disproportionate amount of acute health care resources, composing up to 20% of emergency department (ED) visits,1, 2 having a 2‐fold to 5‐fold increase in likelihood of hospital admission,1 and frequently incurring lengths of hospital stay (LOS) approximately 15% higher than the national averages.3 In addition, they are at increased risk for hospital readmission in the 90‐day interval following hospital discharge.1, 4, 5 Specific risk factors for readmission include age above 80 years, discharge within the previous 30 days, the presence of 3 or more comorbid diagnoses, use of 5 or more prescription medications, difficulty with at least 1 activity of daily living (ADL), and lack of discharge education.6 These risk factors can translate into adverse drug events,79 exacerbations of chronic diseases,10 or functional decline4, 5 that can trigger ED visits or hospital readmission.

Hospital‐based care coordinationdefined as a multidisciplinary interaction between inpatients and providers that focuses on education, communication, and discharge planning with the primary aim of improving outcomeshas demonstrated inconsistent results as a mechanism to reduce LOS, postdischarge ED visits, or hospital readmission rates. While disease‐specific care coordination programs for congestive heart failure and chronic obstructive pulmonary disease have been effective in reducing rehospitalization rates,1015 the benefits of comprehensive care coordination for elderly general medical inpatients with a broader range of diagnoses are less clear. In a group of 750 elderly patients with 1 of 11 common inpatient diagnoses (such as stroke or hip fracture) likely to ultimately require a high level of home support, Coleman et al.16 found that a structured transitional care program centered on a personal coach decreased rehospitalization rates at 30 and 90 days. Preen et al.17 found improved patient involvement and perceived quality of life with care coordination focused on discharge planning, but no impact on LOS. Likewise, a recent meta‐analysis18 failed to demonstrate statistically significant differences in mortality, LOS, or readmission rates in hospitalized patients who received intensive care coordination versus usual care; however, variation in the components of the care coordination intervention and reported outcomes restricted the ability to pool data in this study.

Care coordination programs demonstrating efficacy in reducing health care utilization in elderly medical patients have generally included an outpatient transitional component with out‐of‐hospital postacute care visits by health care personnel such as a nurse, pharmacist, or physician.1923 These offsite interventions generate additional expenses and resource demands that may not be practical for smaller hospitals to implement.24, 25 In contrast, hospital‐based care coordination programs have clear ownership and thus may be more practical to disseminate. Individual elements of hospital‐based care coordination such as pharmacist counseling, discharge education, and telephone follow‐up have been shown to reduce ED visitation and readmission rates in high‐risk elderly patients. Less information is available regarding the impact of these interventions delivered in an aggregate bundle by hospital staff in the absence of bridging transitional visits.2629

The objective of this pilot study was to determine whether a supplemental elderly care bundle, targeted to high‐risk inpatients by hospital staff as an enhancement to existing care coordination, would affect postdischarge readmission and ED visit rates. The intervention was designed to capitalize on existing resources, and focused specifically on elderly inpatients who were hospitalized with diagnoses commonly encountered in a general medical unit and predisposed to recidivism.

Patients and Methods

Delivery of the Supplemental Care Bundle

Starting no later than 24 hours after enrollment and continuing up to 1 week following hospital discharge, intervention group patients received a targeted care bundle provided by 1 of 3 care coordinators (CCs) and 1 of 4 clinical pharmacists (CPs) working with the study team. The care bundle was designed as an intensive patient‐centered educational program that would augment BUMC's existing care coordination processes (delivered to all patients regardless of study participation); specific elements are displayed in Figure 1. Study CCs saw patients daily throughout their hospital stay, and instructed patients on specific health conditions, with an emphasis on optimizing home self‐care and contingency plans if problems arose. CP visits focused on medication reconciliation and education regarding any new agents started during the hospitalization. The personal health record (PHR) provided a tool to engage patients in self‐care, and as discussed by Coleman et al.,7, 16, 33 promoted information transfer from the hospital to outpatient settings. During the postdischarge phone call, CCs followed a basic script to confirm receipt of medical equipment, medications, home health arrangements, and scheduling of follow‐up appointments. They also used this contact as an opportunity to reinforce patient education on managing their conditions. CPs reviewed medication use (type, schedule, dose), and spoke with patients about any symptoms they may have experienced as medication side effects. If indicated based on their phone discussions, both CCs and CPs could recommend an action plan to the patient.

Figure 1

The study CCs and CPs were existing hospital staff and performed their research activities in addition to their usual duties. Study CCs were highly experienced (averaging 8 years of inpatient floor nursing plus 10 years as CCs) and all had advanced nursing certifications (ACM, BSN, or MSN). The CPs were upper‐level pharmacy residents completing their inpatient clinical rotations. Additional training for both study CCs and CPs was limited to a series of 3 meetings (each 45 minutes in duration) regarding the intent and delivery of the supplemental care bundle, including use of study forms.

At the time of the trial, the particular CCs and CPs chosen to deliver the supplemental care bundle had work assignments ensuring that crossover between intervention and usual care groups would not occur. For example, 1 of the study CCs normally covered a surgical floor such that her normal scope of responsibilities would not influence the medical patients in the study (their baseline care coordination was provided by nonstudy personnel). Medication reconciliation and medication education is generally performed by floor nursing staff rather than CPs at BUMC.

Results

The final sample size for this pilot was small, with 41 total patients (21 controls, 20 interventions). The main reason for enrollment failure of patients meeting study criteria was an inability to obtain informed consent. Sixty patients declined participation after being approached, and another 56 patients were unable to give their informed consent due to impairments (poor cognition, medication induced sedation, severity of illness) with lack of an available proxy to give written consent during the 72‐hour postadmission recruitment window. There were no statistically significant differences in the baseline characteristics of the intervention and control groups (Table 2). A similar proportion of patients (23% in the intervention, 15% in controls; P = 0.70) had preexisting diagnoses of dementia or depression. However, on APR‐DRG measures relating to acuity of illness and mortality risk, patients in the intervention group trended toward higher severity (Table 2). Likewise, although it was not a statistically significant difference, 13 of 20 patients in the intervention group were taking medications from 2 drug classes commonly implicated in adverse drug events (warfarin, insulin, diuretics, sedating agents) as part of their discharge medication regimen compared to 10 of 21 patients in the control group.

Study outcomes are displayed in Table 3. Mean LOS is reported as a descriptive finding; there was insufficient power to compare this outcome statistically between groups. The majority of patients were discharged to home. A similar proportion of patients in the intervention (20%) and control groups (22%) who had lived at home immediately prior to admission were discharged from the hospital to skilled care facilities (P = 0.87). The number of readmissions/ED visits (taken as a composite measure of unplanned healthcare utilization) within 30 days of discharge was lower in the intervention group; by 60 days, there was no longer a statistically significant difference in readmission/ED visit rates between groups. For those patients who had a readmission or ED visit following hospital discharge, the intervention group had a longer time interval to first event compared to controls (36.2 versus 15.7 days, P = 0.05). Of the patients discharged to skilled care, 1 in the intervention group (at 53 days) and 1 in the control group (at 16 days) had a readmission/ED visit event. Figure 2 shows time‐to‐first readmission or ED visit event curves at 30 and 60 days for both intervention and control groups. For patients who had a readmission/ED visit event, LOS for this episode was 2.2 2.1 days in controls and 3.7 2.1 days in the intervention group (insufficient power for statistical comparison). The study's small sample size prevented development of a meaningful regression model.

Figure 2

Resource utilization and the specifics of patient‐study personnel interaction associated with the intervention were tracked. Research assistants spent an average of 50 minutes daily screening charts for potential candidates. For the 20 patients who received the supplemental elderly care bundle, study CCs averaged 20 to 25 minutes per patient daily of additional time counseling patients and families, identifying and attending to discharge barriers, filling out documentation, and faxing the supplemental study discharge form to the patient's primary care physician. Any residual home care needs or issues unresolved at discharge were addressed with the patient in the 5 to 7 day follow‐up phone call. Similarly, study CPs expended approximately 20 minutes daily per patient providing medication education, reconciliation, and optimization of drug therapy. Study pharmacists recommended a change to the medication regimens of 10 patients in the intervention group; physicians acted upon these recommendations for 7 of the patients. The changes included dosage adjustment, discontinuation of medications due to possible drug interaction or duplication of drugs with the same pharmacologic effect, and addition of medications as indicated by patient condition or to reconcile with patients' at‐home medication regimens. Patients contacted via phone by the study pharmacist within 1 week after discharge were able to describe proper use of new medications started in the hospital and confirm that they obtained or had the means to obtain the prescribed drugs.

Discussion

This pilot study examined the effects of a supplemental care bundle involving patient education and discharge planning delivered by hospital‐based CCs and CPs on the rate of readmission/ED visitation in 41 elderly (70 years of age) patients. The study was not adequately powered to detect an impact of the intervention on index LOS. The care bundle did lead to significantly fewer readmissions or ED visits 30 days postdischarge and appeared to increase the time interval to first unplanned readmission or ED visit compared to usual care. This effect was no longer present at 60 days postdischarge. Resource allocations and scope of duties for CCs and CPs (an average of 20 minutes per patient per day) related to delivering the intervention were realistic for broader implementation in the hospitalized elderly population at high risk for readmission or ED visitation following discharge.

Length of stay for the initial hospitalization associated with the care bundle was an original outcome of interest to the study team. However, with the final enrollment of 41 patients and a power of 0.8, the between group difference would have needed to be 2.6 days to be statistically significant. It is likely that any change in LOS related to the care bundle would be much smaller, particularly since 2 key determinants of LOS, severity of illness and physician behavior, were beyond this patient education‐oriented intervention's scope of influence.3437 Furthermore, the diverse range of eligible diagnoses limited the study CCs' ability to reduce variability through use of clinical care pathways. One approach in leveraging an elderly care bundle to reduce LOS may be to focus on a specific disease that has well‐established inpatient benchmarks and treatment algorithms. For example, in patients with community‐acquired pneumonia, the use of care coordination in combination with standardized order sets decreased LOS without compromising safety, mainly by shortening the time from clinical stability to discharge.38

On separation of the readmission/ED visit outcome into 30 and 60 day postdischarge time frames, the intervention group had a lower rate of unplanned acute health care use within 30 days postdischarge; the difference between groups had dissipated by 60 days postdischarge. This convergence suggests that a hospital‐based intervention's influence is strongest closer to the time of the initial hospital stay, and wanes as more time has elapsed. Indeed, interventions that have successfully maintained lower readmission rates beyond 60 and 90 days postdischarge in a high‐risk elderly population (such as the program advocated by Coleman et al.16) have included a transitional care provider engaging patients during the hospitalization and performing subsequent visits to the home or nursing facility.33 An optimal intervention would capitalize on the hospital‐based staff's ability to improve short‐term readmission/ED visit rates while linking patients to longer‐term transitional care to extend these outcomes. Electronic health records could potentially facilitate these care transitions, beginning with an automated screening process for identification of high‐risk inpatients and continuing through postdischarge follow‐up. How to develop these resources in settings where outpatient practices are independent or only loosely affiliated with hospitals is an area for continued investigation.

In a group of elderly patients with multiple comorbidities and complex pharmacotherapy regimens, the study bundle component targeting medication management appears to be a high‐yield intervention to reduce unplanned health care utilization following hospital discharge. These patients are more susceptible to nonadherence and drug‐related adverse events, which may contribute to hospital readmission or ED visitation.7, 9, 39 Consistent with findings at other sites,28, 40 a heightened level of CP involvement in the care of high‐risk elderly patients may have helped reduce these undesirable outcomes. Of the 9 readmission/ED visit events in the control group, 3 were attributable to medication related complications (2 from sedatives, 1 from a diuretic). None of the readmission/ED visit events in intervention group patients stemmed from medication effects.

Correspondingly, the research CCs' provision of daily condition‐specific education, additional time to more thoroughly investigate discharge needs, engagement of patients' families as active partners in self‐care, and the use of a structured discharge form along with follow‐up phone calls may have better prepared patients to manage their health problems once released from the hospital.26, 28, 29 For example, 1 patient in the control group was readmitted less than 24 hours after initial discharge due to inability to perform self‐care at home. Given the study power issues described previously, data on LOS for the second hospitalization for patients who had a readmission event are difficult to interpret, but could suggest the occurrence of some shorter, preventable readmissions in the control group. Conversely, the readmission/ED visit events in intervention patients appeared to be associated with a specific medical condition (eg, failure of diabetic cellulitis to respond to appropriate outpatient treatment) rather than problems that would have been corrected with an educational/self‐management program such as this targeted care bundle.

This pilot study had several limitations. The main issue was a small patient sample size that was primarily due to an inability to obtain informed consent. Design of the study as a randomized controlled trial and plans to disseminate study findings beyond BHCS necessitated IRB approval rather than delivery of the supplemental care bundle as a quality improvement (QI) project. Placing QI initiatives under research regulations can lead to project delays, higher costs, and patient frustrations with the process.41, 42 This tension was evident during study screening and enrollment, as many patients who otherwise met criteria and would potentially benefit from the intervention were hesitant to participate in a research study or refused to sign a multipage consent document. The difficulties of enrolling elderly patients in clinical trials have been well‐described.43, 44 Further research involving a minimal‐risk, educational intervention such as this elderly care‐bundle would likely better fit under the category of expedited IRB review with waiver or modification of the informed consent process.45

Incomplete blinding could have potentially affected our results. At the study site, the team members delivering the care bundle were a regular part of the hospital staff (as opposed to external researchers), and it is not unusual for a CC or a pharmacist to enter a patient's room (eg, to confirm a drug allergy history). In view of this, the impact of imperfect blinding on 30‐day outcomes would likely be minimal. Furthermore, a floor staff perception that a specific patient was being taken care of by the study team resulting in a lower than usual level of care, would tend to bias the result of the intervention toward the null effect.

vThe study cohort did not have enough subjects to perform analyses (ie, modeling or examination of subgroups) beyond basic comparative findings. Issues such as preadmission living situation and the presence of depression or cognitive impairment (Mini‐Mental Status Exams were not performed on these patients) may potentially influence postdischarge recidivism; their effects can not be reliably ascertained from these data. Additionally, to prevent study personnel from engaging patients who would soon be going home, it was felt that the benefits from the care bundle would be recognized only if the intervention could be initiated within 72 hours of admission and delivered in full, a requirement that further reduced the enrollment pool. The intent of this pilot work was to guide future investigations surrounding hospital‐to‐home transitional care. The next phase of research in this area will need an enhanced sample size with more extensive baseline data collection so that potential confounding factors or outcomes in specific populations can be explored.

Another problem restricting applicability of study findings was the use of only 3 different CCs and 3 pharmacists on the research team to deliver the components of patient education, discharge planning, and medication counseling in the elderly care bundle. Personnel for the trial were chosen for their experience and interest in the area of care transitions. To distinguish the benefit of the elderly care bundle in general versus the expertise of these particular CCs and study pharmacists, a larger‐scale, multisite trial would be necessary. Lastly, due to resource constraints, patients who resided in long‐term care (either LTAC, SNFs, or nursing homes) prior to admission with anticipated return to those sites were not eligible for the study. Similar to the patients whose comorbidities or acute severity of illness prevented informed consent, this segment of the elderly population may have derived even more benefit from receipt of the elderly care bundle.10, 15, 46 Despite exclusion of this group (which would be expected to lessen the impact of the intervention), a difference in readmission/ED visits rates at 30 days following discharge was observed.

Conclusions

This pilot randomized clinical trial (RCT) evaluated the effects of a supplemental, aggregate care bundle centered on patient education, discharge planning, and medication counseling and reconciliation compared to usual care in a group of elderly patients at high risk of readmission or ED visitation following an index hospitalization. The intervention was designed to be reproducible and make use of existing hospital resources. Probably through facilitation of patient self‐care and home management, the elderly care bundle reduced the composite outcome of readmission/ED visits at 30 days postdischarge. By 60 days, this effect had waned, demonstrating the short‐term benefit of a hospital‐based educational intervention and stressing the need to incorporate additional outpatient transitional care support to sustain favorable outcomes. The study was not powered to detect small differences (which would be more likely than a change of multiple days) in length of index hospital stay related to the care bundle. There were important study limitations (primarily associated with small sample size), and this work should be viewed as hypothesis‐generating. Future trials should assess the impact of a standardized targeted care bundle delivered across multiple healthcare systems on a larger cohort of high‐risk elderly patients, including analysis of financial and personnel allocations relative to the benefits of the intervention.