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Is Posthospital Syndrome a Result of Hospitalization-Induced Allostatic Overload?
After discharge from the hospital, patients have a significantly elevated risk for adverse events, including emergency department use, hospital readmission, and death. More than 1 in 3 patients discharged from the hospital require acute care in the month after hospital discharge, and more than 1 in 6 require readmission, with readmission diagnoses frequently differing from those of the preceding hospitalization.1-4 This heightened susceptibility to adverse events persists beyond 30 days but levels off by 7 weeks after discharge, suggesting that the period of increased risk is transient and dynamic.5
The term posthospital syndrome (PHS) describes this period of vulnerability to major adverse events following hospitalization.6 In addition to increased risk for readmission and mortality, patients in this period often show evidence of generalized dysfunction with new cognitive impairment, mobility disability, or functional decline.7-12 To date, the etiology of this vulnerability is neither well understood nor effectively addressed by transitional care interventions.13
One hypothesis to explain PHS is that stressors associated with the experience of hospitalization contribute to transient multisystem dysfunction that induces susceptibility to a broad range of medical maladies. These stressors include frequent sleep disruption, noxious sounds, painful stimuli, mobility restrictions, and poor nutrition.12 The stress hypothesis as a cause of PHS is therefore based, in large part, on evidence about allostasis and the deleterious effects of allostatic overload.
Allostasis defines a system functioning within normal stress-response parameters to promote adaptation and survival.14 In allostasis, the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic and parasympathetic branches of the autonomic nervous system (ANS) exist in homeostatic balance and respond to environmental stimuli within a range of healthy physiologic parameters. The hallmark of a system in allostasis is the ability to rapidly activate, then successfully deactivate, a stress response once the stressor (ie, threat) has resolved.14,15 To promote survival and potentiate “fight or flight” mechanisms, an appropriate stress response necessarily impacts multiple physiologic systems that result in hemodynamic augmentation and gluconeogenesis to support the anticipated action of large muscle groups, heightened vigilance and memory capabilities to improve rapid decision-making, and enhancement of innate and adaptive immune capabilities to prepare for wound repair and infection defense.14-16 The stress response is subsequently terminated by negative feedback mechanisms of glucocorticoids as well as a shift of the ANS from sympathetic to parasympathetic tone.17,18
Extended or repetitive stress exposure, however, leads to dysregulation of allostatic mechanisms responsible for stress adaptation and hinders an efficient and effective stress response. After extended stress exposure, baseline (ie, resting) HPA activity resets, causing a disruption of normal diurnal cortisol rhythm and an increase in total cortisol concentration. Moreover, in response to stress, HPA and ANS system excitation becomes impaired, and negative feedback properties are undermined.14,15 This maladaptive state, known as allostatic overload, disrupts the finely tuned mechanisms that are the foundation of mind-body balance and yields pathophysiologic consequences to multiple organ systems. Downstream ramifications of allostatic overload include cognitive deterioration, cardiovascular and immune system dysfunction, and functional decline.14,15,19
Although a stress response is an expected and necessary aspect of acute illness that promotes survival, the central thesis of this work is that additional environmental and social stressors inherent in hospitalization may unnecessarily compound stress and increase the risk of HPA axis dysfunction, allostatic overload, and subsequent multisystem dysfunction, predisposing individuals to adverse outcomes after hospital discharge. Based on data from both human subjects and animal models, we present a possible pathophysiologic mechanism for the postdischarge vulnerability of PHS, encourage critical contemplation of traditional hospitalization, and suggest interventions that might improve outcomes.
POSTHOSPITAL SYNDROME
Posthospital syndrome (PHS) describes a transient period of vulnerability after hospitalization during which patients are at elevated risk for adverse events from a broad range of conditions. In support of this characterization, epidemiologic data have demonstrated high rates of adverse outcomes following hospitalization. For example, data have shown that more than 1 in 6 older adults is readmitted to the hospital within 30 days of discharge.20 Death is also common in this first month, during which rates of postdischarge mortality may exceed initial inpatient mortality.21,22 Elevated vulnerability after hospitalization is not restricted to older adults, as readmission risk among younger patients 18 to 64 years of age may be even higher for selected conditions, such as heart failure.3,23
Vulnerability after hospitalization is broad. In patients over age 65 initially admitted for heart failure or acute myocardial infarction, only 35% and 10% of readmissions are for recurrent heart failure or reinfarction, respectively.1 Nearly half of readmissions are for noncardiovascular causes.1 Similarly, following hospitalization for pneumonia, more than 60 percent of readmissions are for nonpulmonary etiologies. Moreover, the risk for all these causes of readmission is much higher than baseline risk, indicating an extended period of lack of resilience to many types of illness.24 These patterns of broad susceptibility also extend to younger adults hospitalized with common medical conditions.3
Accumulating evidence suggests that hospitalized patients face functional decline, debility, and risk for adverse events despite resolution of the presenting illness, implying perhaps that the hospital environment itself is hazardous to patients’ health. In 1993, Creditor hypothesized that the “hazards of hospitalization,” including enforced bed-rest, sensory deprivation, social isolation, and malnutrition lead to a “cascade of dependency” in which a collection of small insults to multiple organ systems precipitates loss of function and debility despite cure or resolution of presenting illness.12 Covinsky (2011) later defined hospitalization-associated disability as an iatrogenic hospital-related “disorder” characterized by new impairments in abilities to perform basic activities of daily living such as bathing, feeding, toileting, dressing, transferring, and walking at the time of hospital discharge.11 Others have described a postintensive-care syndrome (PICS),25 characterized by cognitive, psychiatric, and physical impairments acquired during hospitalization for critical illness that persist postdischarge and increase the long-term risk for adverse outcomes, including elevated mortality rates,26,27 readmission rates,28 and physical disabilities.29 Similar to the “hazards of hospitalization,” PICS is thought to be related to common experiences of ICU stays, including mobility restriction, sensory deprivation, sleep disruption, sedation, malnutrition, and polypharmacy.30-33
Taken together, these data suggest that adverse health consequences attributable to hospitalization extend across the spectrum of age, presenting disease severity, and hospital treatment location. As detailed below, the PHS hypothesis is rooted in a mechanistic understanding of the role of exogenous stressors in producing physiologic dysregulation and subsequent adverse health effects across multiple organ systems.
Nature of Stress in the Hospital
Compounding the stress of acute illness, hospitalized patients are routinely and repetitively exposed to a wide variety of environmental stressors that may have downstream adverse consequences (Table 1). In the absence of overt clinical manifestations of harm, the possible subclinical physiologic dysfunction generated by the following stress exposures may increase patients’ susceptibility to the manifestations of PHS.
Sleep Disruption
Sleep disruptions trigger potent stress responses,34,35 yet they are common occurrences during hospitalization. In surveys, about half of patients report poor sleep quality during hospitalization that persists for many months after discharge.36 In a simulated hospital setting, test subjects exposed to typical hospital sounds (paging system, machine alarms, etc.) experienced significant sleep-wake cycle abnormalities.37 Although no work has yet focused specifically on the physiologic consequences of sleep disruption and stress in hospitalized patients, in healthy humans, mild sleep disruption has clear effects on allostasis by disrupting HPA activity, raising cortisol levels, diminishing parasympathetic tone, and impairing cognitive performance.18,34,35,38,39
Malnourishment
Malnourishment in hospitalized patients is common, with one-fifth of hospitalized patients receiving nothing per mouth or clear liquid diets for more than 3 continuous days,40 and one-fifth of hospitalized elderly patients receiving less than half of their calculated nutrition requirements.41 Although the relationship between food restriction, cortisol levels, and postdischarge outcomes has not been fully explored, in healthy humans, meal anticipation, meal withdrawal (withholding an expected meal), and self-reported dietary restraint are known to generate stress responses.42,43 Furthermore, malnourishment during hospitalization is associated with increased 90-day and 1-year mortality after discharge,44 adding malnourishment to the list of plausible components of hospital-related stress.
Mobility Restriction
Physical activity counterbalances stress responses and minimizes downstream consequences of allostatic load,15 yet mobility limitations via physical and chemical restraints are common in hospitalized patients, particularly among the elderly.45-47 Many patients are tethered to devices that make ambulation hazardous, such as urinary catheters and infusion pumps. Even without physical or chemical restraints or a limited mobility order, patients may be hesitant to leave the room so as not to miss transport to a diagnostic study or an unscheduled physician’s visit. Indeed, mobility limitations of hospitalized patients increase the risk for adverse events after discharge, while interventions designed to encourage mobility are associated with improved postdischarge outcomes.47,48
Other Stressors
Other hospital-related aversive stimuli are less commonly quantified, but clearly exist. According to surveys of hospitalized patients, sources of emotional stress include social isolation; loss of autonomy and privacy; fear of serious illness; lack of control over activities of daily living; lack of clear communication between treatment team and patients; and death of a patient roommate.49,50 Furthermore, consider the physical discomfort and emotional distress of patients with urinary incontinence awaiting assistance for a diaper or bedding change or the pain of repetitive blood draws or other invasive testing. Although individualized, the subjective discomfort and emotional distress associated with these experiences undoubtedly contribute to the stress of hospitalization.
IMPACT OF ALLOSTATIC OVERLOAD ON PHYSIOLOGIC FUNCTION
Animal Models of Stress
Laboratory techniques reminiscent of the numerous environmental stressors associated with hospitalization have been used to reliably trigger allostatic overload in healthy young animals.51 These techniques include sequential exposure to aversive stimuli, including food and water deprivation, continuous overnight illumination, paired housing with known and unknown cagemates, mobility restriction, soiled cage conditions, and continuous noise. All of these techniques have been shown to cause HPA axis and ANS dysfunction, allostatic overload, and subsequent stress-mediated consequences to multiple organ systems.19,52-54 Given the remarkable similarity of these protocols to common experiences during hospitalization, animal models of stress may be useful in understanding the spectrum of maladaptive consequences experienced by patients within the hospital (Figure 1).
These animal models of stress have resulted in a number of instructive findings. For example, in rodents, extended stress exposure induces structural and functional remodeling of neuronal networks that precipitate learning and memory, working memory, and attention impairments.55-57 These exposures also result in cardiovascular abnormalities, including dyslipidemia, progressive atherosclerosis,58,59 and enhanced inflammatory cytokine expression,60 all of which increase both atherosclerotic burden and susceptibility to plaque rupture, leading to elevated risk for major cardiovascular adverse events. Moreover, these extended stress exposures in animals increase susceptibility to both bacterial and viral infections and increase their severity.16,61 This outcome appears to be driven by a stress-induced elevation of glucocorticoid levels, decreased leukocyte proliferation, altered leukocyte trafficking, and a transition to a proinflammatory cytokine environment.16, 61 Allostatic overload has also been shown to contribute to metabolic dysregulation involving insulin resistance, persistence of hyperglycemia, dyslipidemia, catabolism of lean muscle, and visceral adipose tissue deposition.62-64 In addition to cardiovascular, immune, and metabolic consequences of allostatic overload, the spectrum of physiologic dysfunction in animal models is broad and includes mood disorder symptoms,65 intestinal barrier abnormalities,66 airway reactivity exacerbation,67 and enhanced tumor growth.68
Although the majority of this research highlights the multisystem effects of variable stress exposure in healthy animals, preliminary evidence suggests that aged or diseased animals subjected to additional stressors display a heightened inflammatory cytokine response that contributes to exaggerated sickness behavior and greater and prolonged cognitive deficits.69 Future studies exploring the consequences of extended stress exposure in animals with existing disease or debility may therefore more closely simulate the experience of hospitalized patients and perhaps further our understanding of PHS.
Hospitalized Patients
While no intervention studies have examined the effects of potential hospital stressors on the development of allostatic overload, there is evidence from small studies that dysregulated stress responses during hospitalization are associated with adverse events. For example, high serum cortisol, catecholamine, and proinflammatory cytokine levels during hospitalization have individually been associated with the development of cognitive dysfunction,70-72 increased risk of cardiovascular events such as myocardial infarction and stroke in the year following discharge,73-76 and the development of wound infections after discharge.77 Moreover, elevated plasma glucose during admission for myocardial infarction in patients with or without diabetes has been associated with greater in-hospital and 1-year mortality,78 with a similar relationship seen between elevated plasma glucose and survival after admission for stroke79 and pneumonia.80 Furthermore, in addition to atherothrombosis, stress may contribute to the risk for venous thromboembolism,81 resulting in readmissions for deep vein thrombosis or pulmonary embolism posthospitalization. Although potentially surrogate markers of illness acuity, a handful of studies have shown that these stress biomarkers are actually only weakly correlated with,82 or independent of,72,76 disease severity. As discussed in detail below, future studies utilizing a summative measure of multisystem physiologic dysfunction as opposed to individual biomarkers may more accurately reflect the cumulative stress effects of hospitalization and subsequent risk for adverse events.
Additional Considerations
Elderly patients, in particular, may have heightened susceptibility to the consequences of allostatic overload due to common geriatric issues such as multimorbidity and frailty. Patients with chronic diseases display both baseline HPA axis abnormalities as well as dysregulated stress responses and may therefore be more vulnerable to hospitalization-related stress. For example, when subjected to psychosocial stress, patients with chronic conditions such as diabetes, heart failure, or atherosclerosis demonstrate elevated cortisol levels, increased circulating markers of inflammation, as well as prolonged hemodynamic recovery after stress resolution compared with normal controls.83-85 Additionally, frailty may affect an individual’s susceptibility to exogenous stress. Indeed, frailty identified on hospital admission increases the risk for adverse outcomes during hospitalization and postdischarge.86 Although the specific etiology of this relationship is unclear, persons with frailty are known to have elevated levels of cortisol and other inflammatory markers,87,88 which may contribute to adverse outcomes in the face of additional stressors.
IMPLICATIONS AND NEXT STEPS
A large body of evidence stretching from bench to bedside suggests that environmental stressors associated with hospitalization are toxic. Understanding PHS within the context of hospital-induced allostatic overload presents a unifying theory for the interrelated multisystem dysfunction and increased susceptibility to adverse events that patients experience after discharge (Figure 2). Furthermore, it defines a potential pathophysiological mechanism for the cognitive impairment, elevated cardiovascular risk, immune system dysfunction, metabolic derangements, and functional decline associated with PHS. Additionally, this theory highlights environmental interventions to limit PHS development and suggests mechanisms to promote stress resilience. Although it is difficult to disentangle the consequences of the endogenous stress triggered by an acute illness from the exogenous stressors related to hospitalization, it is likely that the 2 simultaneous exposures compound risk for stress system dysregulation and allostatic overload. Moreover, hospitalized patients with preexisting HPA axis dysfunction at baseline from chronic disease or advancing age may be even more susceptible to these adverse outcomes. If this hypothesis is true, a reduction in PHS would require mitigation of the modifiable environmental stressors encountered by patients during hospitalization. Directed efforts to diminish ambient noise, limit nighttime disruptions, thoughtfully plan procedures, consider ongoing nutritional status, and promote opportunities for patients to exert some control over their environment may diminish the burden of extrinsic stressors encountered by all patients in the hospital and improve outcomes after discharge.
Hospitals are increasingly recognizing the importance of improving patients’ experience of hospitalization by reducing exposure to potential toxicities. For example, many hospitals are now attempting to reduce sleep disturbances and sleep latency through reduced nighttime noise and light levels, fewer nighttime interruptions for vital signs checks and medication administration, and commonsensical interventions like massages, herbal teas, and warm milk prior to bedtime.89 Likewise, intensive care units are targeting environmental and physical stressors with a multifaceted approach to decrease sedative use, promote healthy sleep cycles, and encourage exercise and ambulation even in those patients who are mechanically ventilated.30 Another promising development has been the increase of Hospital at Home programs. In these programs, patients who meet the criteria for inpatient admission are instead comprehensively managed at home for their acute illness through a multidisciplinary effort between physicians, nurses, social workers, physical therapists, and others. Patients hospitalized at home report higher levels of satisfaction and have modest functional gains, improved health-related quality of life, and decreased risk of mortality at 6 months compared with hospitalized patients.90,91 With some admitting diagnoses (eg, heart failure), hospitalization at home may be associated with decreased readmission risk.92 Although not yet investigated on a physiologic level, perhaps the benefits of hospital at home are partially due to the dramatic difference in exposure to environmental stressors.
A tool that quantifies hospital-associated stress may help health providers appreciate the experience of patients and better target interventions to aspects of their structure and process that contribute to allostatic overload. Importantly, allostatic overload cannot be identified by one biomarker of stress but instead requires evidence of dysregulation across inflammatory, neuroendocrine, hormonal, and cardiometabolic systems. Future studies to address the burden of stress faced by hospitalized patients should consider a summative measure of multisystem dysregulation as opposed to isolated assessments of individual biomarkers. Allostatic load has previously been operationalized as the summation of a variety of hemodynamic, hormonal, and metabolic factors, including blood pressure, lipid profile, glycosylated hemoglobin, cortisol, catecholamine levels, and inflammatory markers.93 To develop a hospital-associated allostatic load index, models should ideally be adjusted for acute illness severity, patient-reported stress, and capacity for stress resilience. This tool could then be used to quantify hospitalization-related allostatic load and identify those at greatest risk for adverse events after discharge, as well as measure the effectiveness of strategic environmental interventions (Table 2). A natural first experiment may be a comparison of the allostatic load of hospitalized patients versus those hospitalized at home.
The risk of adverse outcomes after discharge is likely a function of the vulnerability of the patient and the degree to which the patient’s healthcare team and social support network mitigates this vulnerability. That is, there is a risk that a person struggles in the postdischarge period and, in many circumstances, a strong healthcare team and social network can identify health problems early and prevent them from progressing to the point that they require hospitalization.13,94-96 There are also hospital occurrences, outside of allostatic load, that can lead to complications that lengthen the stay, weaken the patient, and directly contribute to subsequent vulnerability.94,97 Our contention is that the allostatic load of hospitalization, which may also vary by patient depending on the circumstances of hospitalization, is just one contributor, albeit potentially an important one, to vulnerability to medical problems after discharge.
In conclusion, a plausible etiology of PHS is the maladaptive mind-body consequences of common stressors during hospitalization that compound the stress of acute illness and produce allostatic overload. This stress-induced dysfunction potentially contributes to a spectrum of generalized disease susceptibility and risk of adverse outcomes after discharge. Focused efforts to diminish patient exposure to hospital-related stressors during and after hospitalization might diminish the presence or severity of PHS. Viewing PHS from this perspective enables the development of hypothesis-driven risk-prediction models, encourages critical contemplation of traditional hospitalization, and suggests that targeted environmental interventions may significantly reduce adverse outcomes.
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53. Krishnan V, Nestler EJ. Animal models of depression: molecular perspectives. Curr Top Behav Neurosci. 2011;7:121-147. http://dx.doi.org/10.1007/7854_2010_108.
54. Magarinos AM, McEwen BS. Stress-induced atrophy of apical dendrites of hippocampal CA3c neurons: comparison of stressors. Neuroscience. 1995;69(1):83-88. http://dx.doi.org/10.1016/0306-4522(95)00256-I.
55. McEwen BS. Plasticity of the hippocampus: adaptation to chronic stress and allostatic load. Ann N Y Acad Sci. 2001;933:265-277. http://dx.doi.org/10.1111/j.1749-6632.2001.tb05830.x.
56. McEwen BS. The brain on stress: toward an integrative approach to brain, body, and behavior. Perspect Psychol Sci. 2013;8(6):673-675. http://dx.doi.org/10.1177/1745691613506907.
57. McEwen BS, Morrison JH. The brain on stress: vulnerability and plasticity of the prefrontal cortex over the life course. Neuron. 2013;79(1):16-29. http://dx.doi.org/10.1016/j.neuron.2013.06.028.
58. Dutta P, Courties G, Wei Y, et al. Myocardial infarction accelerates atherosclerosis. Nature. 2012;487(7407):325-329. http://dx.doi.org/10.1038/nature11260.
59. Lu XT, Liu YF, Zhang L, et al. Unpredictable chronic mild stress promotes atherosclerosis in high cholesterol-fed rabbits. Psychosom Med. 2012;74(6):604-611. http://dx.doi.org/10.1097/PSY.0b013e31825d0b71.
60. Heidt T, Sager HB, Courties G, et al. Chronic variable stress activates hematopoietic stem cells. Nat Med. 2014;20(7):754-758. http://dx.doi.org/10.1038/nm.3589.
61. Sheridan JF, Feng NG, Bonneau RH, Allen CM, Huneycutt BS, Glaser R. Restraint stress differentially affects anti-viral cellular and humoral immune responses in mice. J Neuroimmunol. 1991;31(3):245-255. http://dx.doi.org/10.1016/0165-5728(91)90046-A.
62. Kyrou I, Tsigos C. Stress hormones: physiological stress and regulation of metabolism. Curr Opin Pharmacol. 2009;9(6):787-793. http://dx.doi.org/10.1016/j.coph.2009.08.007.
63. Rosmond R. Role of stress in the pathogenesis of the metabolic syndrome. Psychoneuroendocrinology. 2005;30(1):1-10. http://dx.doi.org/10.1016/j.psyneuen.2004.05.007.
64. Tamashiro KL, Sakai RR, Shively CA, Karatsoreos IN, Reagan LP. Chronic stress, metabolism, and metabolic syndrome. Stress. 2011;14(5):468-474. http://dx.doi.org/10.3109/10253890.2011.606341.
65. McEwen BS. Mood disorders and allostatic load. Biol Psychiatry. 2003;54(3):200-207. http://dx.doi.org/10.1016/S0006-3223(03)00177-X.
66. Zareie M, Johnson-Henry K, Jury J, et al. Probiotics prevent bacterial translocation and improve intestinal barrier function in rats following chronic psychological stress. Gut. 2006;55(11):1553-1560. http://dx.doi.org/10.1136/gut.2005.080739.
67. Joachim RA, Quarcoo D, Arck PC, Herz U, Renz H, Klapp BF. Stress enhances airway reactivity and airway inflammation in an animal model of allergic bronchial asthma. Psychosom Med. 2003;65(5):811-815. http://dx.doi.org/10.1097/01.PSY.0000088582.50468.A3.
68. Thaker PH, Han LY, Kamat AA, et al. Chronic stress promotes tumor growth and angiogenesis in a mouse model of ovarian carcinoma. Nat Med. 2006;12(8):939-944. http://dx.doi.org/10.1038/nm1447.
69. Schreuder L, Eggen BJ, Biber K, Schoemaker RG, Laman JD, de Rooij SE. Pathophysiological and behavioral effects of systemic inflammation in aged and diseased rodents with relevance to delirium: A systematic review. Brain Behav Immun. 2017;62:362-381. http://dx.doi.org/10.1016/j.bbi.2017.01.010.
70. Mu DL, Li LH, Wang DX, et al. High postoperative serum cortisol level is associated with increased risk of cognitive dysfunction early after coronary artery bypass graft surgery: a prospective cohort study. PLoS One. 2013;8(10):e77637. http://dx.doi.org/10.1371/journal.pone.0077637.
71. Mu DL, Wang DX, Li LH, et al. High serum cortisol level is associated with increased risk of delirium after coronary artery bypass graft surgery: a prospective cohort study. Crit Care. 2010;14(6):R238. http://dx.doi.org/10.1186/cc9393.
72. Nguyen DN, Huyghens L, Zhang H, Schiettecatte J, Smitz J, Vincent JL. Cortisol is an associated-risk factor of brain dysfunction in patients with severe sepsis and septic shock. Biomed Res Int. 2014;2014:712742. http://dx.doi.org/10.1155/2014/712742.
73. Elkind MS, Carty CL, O’Meara ES, et al. Hospitalization for infection and risk of acute ischemic stroke: the Cardiovascular Health Study. Stroke. 2011;42(7):1851-1856. http://dx.doi.org/10.1161/STROKEAHA.110.608588.
74. Feibel JH, Hardy PM, Campbell RG, Goldstein MN, Joynt RJ. Prognostic value of the stress response following stroke. JAMA. 1977;238(13):1374-1376.
75. Jutla SK, Yuyun MF, Quinn PA, Ng LL. Plasma cortisol and prognosis of patients with acute myocardial infarction. J Cardiovasc Med (Hagerstown). 2014;15(1):33-41. http://dx.doi.org/10.2459/JCM.0b013e328364100b.
76. Yende S, D’Angelo G, Kellum JA, et al. Inflammatory markers at hospital discharge predict subsequent mortality after pneumonia and sepsis. Am J Respir Crit Care Med. 2008;177(11):1242-1247. http://dx.doi.org/10.1164/rccm.200712-1777OC.
77. Gouin JP, Kiecolt-Glaser JK. The impact of psychological stress on wound healing: methods and mechanisms. Immunol Allergy Clin North Am. 2011;31(1):81-93. http://dx.doi.org/10.1016/j.iac.2010.09.010.
78. Capes SE, Hunt D, Malmberg K, Gerstein HC. Stress hyperglycaemia and increased risk of death after myocardial infarction in patients with and without diabetes: a systematic overview. Lancet. 2000;355(9206):773-778. http://dx.doi.org/10.1016/S0140-6736(99)08415-9.
79. O’Neill PA, Davies I, Fullerton KJ, Bennett D. Stress hormone and blood glucose response following acute stroke in the elderly. Stroke. 1991;22(7):842-847. http://dx.doi.org/10.1161/01.STR.22.7.842.
80. Waterer GW, Kessler LA, Wunderink RG. Medium-term survival after hospitalization with community-acquired pneumonia. Am J Respir Crit Care Med. 2004;169(8):910-914. http://dx.doi.org/10.1164/rccm.200310-1448OC.
81. Rosengren A, Freden M, Hansson PO, Wilhelmsen L, Wedel H, Eriksson H. Psychosocial factors and venous thromboembolism: a long-term follow-up study of Swedish men. J Thrombosis Haemostasis. 2008;6(4):558-564. http://dx.doi.org/10.1111/j.1538-7836.2007.02857.x.
82. Oswald GA, Smith CC, Betteridge DJ, Yudkin JS. Determinants and importance of stress hyperglycaemia in non-diabetic patients with myocardial infarction. BMJ. 1986;293(6552):917-922. http://dx.doi.org/10.1136/bmj.293.6552.917.
83. Middlekauff HR, Nguyen AH, Negrao CE, et al. Impact of acute mental stress on sympathetic nerve activity and regional blood flow in advanced heart failure: implications for ‘triggering’ adverse cardiac events. Circulation. 1997;96(6):1835-1842. http://dx.doi.org/10.1161/01.CIR.96.6.1835.
84. Nijm J, Jonasson L. Inflammation and cortisol response in coronary artery disease. Ann Med. 2009;41(3):224-233. http://dx.doi.org/10.1080/07853890802508934.
85. Steptoe A, Hackett RA, Lazzarino AI, et al. Disruption of multisystem responses to stress in type 2 diabetes: investigating the dynamics of allostatic load. Proc Natl Acad Sci U S A. 2014;111(44):15693-15698. http://dx.doi.org/10.1073/pnas.1410401111.
86. Sepehri A, Beggs T, Hassan A, et al. The impact of frailty on outcomes after cardiac surgery: a systematic review. J Thorac Cardiovasc Surg. 2014;148(6):3110-3117. http://dx.doi.org/10.1016/j.jtcvs.2014.07.087.
87. Johar H, Emeny RT, Bidlingmaier M, et al. Blunted diurnal cortisol pattern is associated with frailty: a cross-sectional study of 745 participants aged 65 to 90 years. J Clin Endocrinol Metab. 2014;99(3):E464-468. http://dx.doi.org/10.1210/jc.2013-3079.
88. Yao X, Li H, Leng SX. Inflammation and immune system alterations in frailty. Clin Geriatr Med. 2011;27(1):79-87. http://dx.doi.org/10.1016/j.cger.2010.08.002.
89. Hospital Elder Life Program (HELP) for Prevention of Delirium. 2017; http://www.hospitalelderlifeprogram.org/. Accessed February 16, 2018.
90. Shepperd S, Doll H, Angus RM, et al. Admission avoidance hospital at home. Cochrane Database of System Rev. 2008;(4):CD007491. http://dx.doi.org/10.1002/14651858.CD007491.pub2’
91. Leff B, Burton L, Mader SL, et al. Comparison of functional outcomes associated with hospital at home care and traditional acute hospital care. J Am Geriatrics Soc. 2009;57(2):273-278. http://dx.doi.org/10.1111/j.1532-5415.2008.02103.x.
92. Qaddoura A, Yazdan-Ashoori P, Kabali C, et al. Efficacy of hospital at home in patients with heart failure: a systematic review and meta-analysis. PloS One. 2015;10(6):e0129282. http://dx.doi.org/10.1371/journal.pone.0129282.
93. Seeman T, Gruenewald T, Karlamangla A, et al. Modeling multisystem biological risk in young adults: The Coronary Artery Risk Development in Young Adults Study. Am J Hum Biol. 2010;22(4):463-472. http://dx.doi.org/10.1002/ajhb.21018.
94. Auerbach AD, Kripalani S, Vasilevskis EE, et al. Preventability and causes of readmissions in a national cohort of general medicine patients. JAMA Intern Med. 2016;176(4):484-493. http://dx.doi.org/10.1001/jamainternmed.2015.7863.
95. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520-528. http://dx.doi.org/10.7326/0003-4819-155-8-201110180-00008.
96. Takahashi PY, Naessens JM, Peterson SM, et al. Short-term and long-term effectiveness of a post-hospital care transitions program in an older, medically complex population. Healthcare. 2016;4(1):30-35. http://dx.doi.org/10.1016/j.hjdsi.2015.06.006.
97. Dharmarajan K, Swami S, Gou RY, Jones RN, Inouye SK. Pathway from delirium to death: potential in-hospital mediators of excess mortality. J Am Geriatr Soc. 2017;65(5):1026-1033. http://dx.doi.org/10.1111/jgs.14743.
After discharge from the hospital, patients have a significantly elevated risk for adverse events, including emergency department use, hospital readmission, and death. More than 1 in 3 patients discharged from the hospital require acute care in the month after hospital discharge, and more than 1 in 6 require readmission, with readmission diagnoses frequently differing from those of the preceding hospitalization.1-4 This heightened susceptibility to adverse events persists beyond 30 days but levels off by 7 weeks after discharge, suggesting that the period of increased risk is transient and dynamic.5
The term posthospital syndrome (PHS) describes this period of vulnerability to major adverse events following hospitalization.6 In addition to increased risk for readmission and mortality, patients in this period often show evidence of generalized dysfunction with new cognitive impairment, mobility disability, or functional decline.7-12 To date, the etiology of this vulnerability is neither well understood nor effectively addressed by transitional care interventions.13
One hypothesis to explain PHS is that stressors associated with the experience of hospitalization contribute to transient multisystem dysfunction that induces susceptibility to a broad range of medical maladies. These stressors include frequent sleep disruption, noxious sounds, painful stimuli, mobility restrictions, and poor nutrition.12 The stress hypothesis as a cause of PHS is therefore based, in large part, on evidence about allostasis and the deleterious effects of allostatic overload.
Allostasis defines a system functioning within normal stress-response parameters to promote adaptation and survival.14 In allostasis, the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic and parasympathetic branches of the autonomic nervous system (ANS) exist in homeostatic balance and respond to environmental stimuli within a range of healthy physiologic parameters. The hallmark of a system in allostasis is the ability to rapidly activate, then successfully deactivate, a stress response once the stressor (ie, threat) has resolved.14,15 To promote survival and potentiate “fight or flight” mechanisms, an appropriate stress response necessarily impacts multiple physiologic systems that result in hemodynamic augmentation and gluconeogenesis to support the anticipated action of large muscle groups, heightened vigilance and memory capabilities to improve rapid decision-making, and enhancement of innate and adaptive immune capabilities to prepare for wound repair and infection defense.14-16 The stress response is subsequently terminated by negative feedback mechanisms of glucocorticoids as well as a shift of the ANS from sympathetic to parasympathetic tone.17,18
Extended or repetitive stress exposure, however, leads to dysregulation of allostatic mechanisms responsible for stress adaptation and hinders an efficient and effective stress response. After extended stress exposure, baseline (ie, resting) HPA activity resets, causing a disruption of normal diurnal cortisol rhythm and an increase in total cortisol concentration. Moreover, in response to stress, HPA and ANS system excitation becomes impaired, and negative feedback properties are undermined.14,15 This maladaptive state, known as allostatic overload, disrupts the finely tuned mechanisms that are the foundation of mind-body balance and yields pathophysiologic consequences to multiple organ systems. Downstream ramifications of allostatic overload include cognitive deterioration, cardiovascular and immune system dysfunction, and functional decline.14,15,19
Although a stress response is an expected and necessary aspect of acute illness that promotes survival, the central thesis of this work is that additional environmental and social stressors inherent in hospitalization may unnecessarily compound stress and increase the risk of HPA axis dysfunction, allostatic overload, and subsequent multisystem dysfunction, predisposing individuals to adverse outcomes after hospital discharge. Based on data from both human subjects and animal models, we present a possible pathophysiologic mechanism for the postdischarge vulnerability of PHS, encourage critical contemplation of traditional hospitalization, and suggest interventions that might improve outcomes.
POSTHOSPITAL SYNDROME
Posthospital syndrome (PHS) describes a transient period of vulnerability after hospitalization during which patients are at elevated risk for adverse events from a broad range of conditions. In support of this characterization, epidemiologic data have demonstrated high rates of adverse outcomes following hospitalization. For example, data have shown that more than 1 in 6 older adults is readmitted to the hospital within 30 days of discharge.20 Death is also common in this first month, during which rates of postdischarge mortality may exceed initial inpatient mortality.21,22 Elevated vulnerability after hospitalization is not restricted to older adults, as readmission risk among younger patients 18 to 64 years of age may be even higher for selected conditions, such as heart failure.3,23
Vulnerability after hospitalization is broad. In patients over age 65 initially admitted for heart failure or acute myocardial infarction, only 35% and 10% of readmissions are for recurrent heart failure or reinfarction, respectively.1 Nearly half of readmissions are for noncardiovascular causes.1 Similarly, following hospitalization for pneumonia, more than 60 percent of readmissions are for nonpulmonary etiologies. Moreover, the risk for all these causes of readmission is much higher than baseline risk, indicating an extended period of lack of resilience to many types of illness.24 These patterns of broad susceptibility also extend to younger adults hospitalized with common medical conditions.3
Accumulating evidence suggests that hospitalized patients face functional decline, debility, and risk for adverse events despite resolution of the presenting illness, implying perhaps that the hospital environment itself is hazardous to patients’ health. In 1993, Creditor hypothesized that the “hazards of hospitalization,” including enforced bed-rest, sensory deprivation, social isolation, and malnutrition lead to a “cascade of dependency” in which a collection of small insults to multiple organ systems precipitates loss of function and debility despite cure or resolution of presenting illness.12 Covinsky (2011) later defined hospitalization-associated disability as an iatrogenic hospital-related “disorder” characterized by new impairments in abilities to perform basic activities of daily living such as bathing, feeding, toileting, dressing, transferring, and walking at the time of hospital discharge.11 Others have described a postintensive-care syndrome (PICS),25 characterized by cognitive, psychiatric, and physical impairments acquired during hospitalization for critical illness that persist postdischarge and increase the long-term risk for adverse outcomes, including elevated mortality rates,26,27 readmission rates,28 and physical disabilities.29 Similar to the “hazards of hospitalization,” PICS is thought to be related to common experiences of ICU stays, including mobility restriction, sensory deprivation, sleep disruption, sedation, malnutrition, and polypharmacy.30-33
Taken together, these data suggest that adverse health consequences attributable to hospitalization extend across the spectrum of age, presenting disease severity, and hospital treatment location. As detailed below, the PHS hypothesis is rooted in a mechanistic understanding of the role of exogenous stressors in producing physiologic dysregulation and subsequent adverse health effects across multiple organ systems.
Nature of Stress in the Hospital
Compounding the stress of acute illness, hospitalized patients are routinely and repetitively exposed to a wide variety of environmental stressors that may have downstream adverse consequences (Table 1). In the absence of overt clinical manifestations of harm, the possible subclinical physiologic dysfunction generated by the following stress exposures may increase patients’ susceptibility to the manifestations of PHS.
Sleep Disruption
Sleep disruptions trigger potent stress responses,34,35 yet they are common occurrences during hospitalization. In surveys, about half of patients report poor sleep quality during hospitalization that persists for many months after discharge.36 In a simulated hospital setting, test subjects exposed to typical hospital sounds (paging system, machine alarms, etc.) experienced significant sleep-wake cycle abnormalities.37 Although no work has yet focused specifically on the physiologic consequences of sleep disruption and stress in hospitalized patients, in healthy humans, mild sleep disruption has clear effects on allostasis by disrupting HPA activity, raising cortisol levels, diminishing parasympathetic tone, and impairing cognitive performance.18,34,35,38,39
Malnourishment
Malnourishment in hospitalized patients is common, with one-fifth of hospitalized patients receiving nothing per mouth or clear liquid diets for more than 3 continuous days,40 and one-fifth of hospitalized elderly patients receiving less than half of their calculated nutrition requirements.41 Although the relationship between food restriction, cortisol levels, and postdischarge outcomes has not been fully explored, in healthy humans, meal anticipation, meal withdrawal (withholding an expected meal), and self-reported dietary restraint are known to generate stress responses.42,43 Furthermore, malnourishment during hospitalization is associated with increased 90-day and 1-year mortality after discharge,44 adding malnourishment to the list of plausible components of hospital-related stress.
Mobility Restriction
Physical activity counterbalances stress responses and minimizes downstream consequences of allostatic load,15 yet mobility limitations via physical and chemical restraints are common in hospitalized patients, particularly among the elderly.45-47 Many patients are tethered to devices that make ambulation hazardous, such as urinary catheters and infusion pumps. Even without physical or chemical restraints or a limited mobility order, patients may be hesitant to leave the room so as not to miss transport to a diagnostic study or an unscheduled physician’s visit. Indeed, mobility limitations of hospitalized patients increase the risk for adverse events after discharge, while interventions designed to encourage mobility are associated with improved postdischarge outcomes.47,48
Other Stressors
Other hospital-related aversive stimuli are less commonly quantified, but clearly exist. According to surveys of hospitalized patients, sources of emotional stress include social isolation; loss of autonomy and privacy; fear of serious illness; lack of control over activities of daily living; lack of clear communication between treatment team and patients; and death of a patient roommate.49,50 Furthermore, consider the physical discomfort and emotional distress of patients with urinary incontinence awaiting assistance for a diaper or bedding change or the pain of repetitive blood draws or other invasive testing. Although individualized, the subjective discomfort and emotional distress associated with these experiences undoubtedly contribute to the stress of hospitalization.
IMPACT OF ALLOSTATIC OVERLOAD ON PHYSIOLOGIC FUNCTION
Animal Models of Stress
Laboratory techniques reminiscent of the numerous environmental stressors associated with hospitalization have been used to reliably trigger allostatic overload in healthy young animals.51 These techniques include sequential exposure to aversive stimuli, including food and water deprivation, continuous overnight illumination, paired housing with known and unknown cagemates, mobility restriction, soiled cage conditions, and continuous noise. All of these techniques have been shown to cause HPA axis and ANS dysfunction, allostatic overload, and subsequent stress-mediated consequences to multiple organ systems.19,52-54 Given the remarkable similarity of these protocols to common experiences during hospitalization, animal models of stress may be useful in understanding the spectrum of maladaptive consequences experienced by patients within the hospital (Figure 1).
These animal models of stress have resulted in a number of instructive findings. For example, in rodents, extended stress exposure induces structural and functional remodeling of neuronal networks that precipitate learning and memory, working memory, and attention impairments.55-57 These exposures also result in cardiovascular abnormalities, including dyslipidemia, progressive atherosclerosis,58,59 and enhanced inflammatory cytokine expression,60 all of which increase both atherosclerotic burden and susceptibility to plaque rupture, leading to elevated risk for major cardiovascular adverse events. Moreover, these extended stress exposures in animals increase susceptibility to both bacterial and viral infections and increase their severity.16,61 This outcome appears to be driven by a stress-induced elevation of glucocorticoid levels, decreased leukocyte proliferation, altered leukocyte trafficking, and a transition to a proinflammatory cytokine environment.16, 61 Allostatic overload has also been shown to contribute to metabolic dysregulation involving insulin resistance, persistence of hyperglycemia, dyslipidemia, catabolism of lean muscle, and visceral adipose tissue deposition.62-64 In addition to cardiovascular, immune, and metabolic consequences of allostatic overload, the spectrum of physiologic dysfunction in animal models is broad and includes mood disorder symptoms,65 intestinal barrier abnormalities,66 airway reactivity exacerbation,67 and enhanced tumor growth.68
Although the majority of this research highlights the multisystem effects of variable stress exposure in healthy animals, preliminary evidence suggests that aged or diseased animals subjected to additional stressors display a heightened inflammatory cytokine response that contributes to exaggerated sickness behavior and greater and prolonged cognitive deficits.69 Future studies exploring the consequences of extended stress exposure in animals with existing disease or debility may therefore more closely simulate the experience of hospitalized patients and perhaps further our understanding of PHS.
Hospitalized Patients
While no intervention studies have examined the effects of potential hospital stressors on the development of allostatic overload, there is evidence from small studies that dysregulated stress responses during hospitalization are associated with adverse events. For example, high serum cortisol, catecholamine, and proinflammatory cytokine levels during hospitalization have individually been associated with the development of cognitive dysfunction,70-72 increased risk of cardiovascular events such as myocardial infarction and stroke in the year following discharge,73-76 and the development of wound infections after discharge.77 Moreover, elevated plasma glucose during admission for myocardial infarction in patients with or without diabetes has been associated with greater in-hospital and 1-year mortality,78 with a similar relationship seen between elevated plasma glucose and survival after admission for stroke79 and pneumonia.80 Furthermore, in addition to atherothrombosis, stress may contribute to the risk for venous thromboembolism,81 resulting in readmissions for deep vein thrombosis or pulmonary embolism posthospitalization. Although potentially surrogate markers of illness acuity, a handful of studies have shown that these stress biomarkers are actually only weakly correlated with,82 or independent of,72,76 disease severity. As discussed in detail below, future studies utilizing a summative measure of multisystem physiologic dysfunction as opposed to individual biomarkers may more accurately reflect the cumulative stress effects of hospitalization and subsequent risk for adverse events.
Additional Considerations
Elderly patients, in particular, may have heightened susceptibility to the consequences of allostatic overload due to common geriatric issues such as multimorbidity and frailty. Patients with chronic diseases display both baseline HPA axis abnormalities as well as dysregulated stress responses and may therefore be more vulnerable to hospitalization-related stress. For example, when subjected to psychosocial stress, patients with chronic conditions such as diabetes, heart failure, or atherosclerosis demonstrate elevated cortisol levels, increased circulating markers of inflammation, as well as prolonged hemodynamic recovery after stress resolution compared with normal controls.83-85 Additionally, frailty may affect an individual’s susceptibility to exogenous stress. Indeed, frailty identified on hospital admission increases the risk for adverse outcomes during hospitalization and postdischarge.86 Although the specific etiology of this relationship is unclear, persons with frailty are known to have elevated levels of cortisol and other inflammatory markers,87,88 which may contribute to adverse outcomes in the face of additional stressors.
IMPLICATIONS AND NEXT STEPS
A large body of evidence stretching from bench to bedside suggests that environmental stressors associated with hospitalization are toxic. Understanding PHS within the context of hospital-induced allostatic overload presents a unifying theory for the interrelated multisystem dysfunction and increased susceptibility to adverse events that patients experience after discharge (Figure 2). Furthermore, it defines a potential pathophysiological mechanism for the cognitive impairment, elevated cardiovascular risk, immune system dysfunction, metabolic derangements, and functional decline associated with PHS. Additionally, this theory highlights environmental interventions to limit PHS development and suggests mechanisms to promote stress resilience. Although it is difficult to disentangle the consequences of the endogenous stress triggered by an acute illness from the exogenous stressors related to hospitalization, it is likely that the 2 simultaneous exposures compound risk for stress system dysregulation and allostatic overload. Moreover, hospitalized patients with preexisting HPA axis dysfunction at baseline from chronic disease or advancing age may be even more susceptible to these adverse outcomes. If this hypothesis is true, a reduction in PHS would require mitigation of the modifiable environmental stressors encountered by patients during hospitalization. Directed efforts to diminish ambient noise, limit nighttime disruptions, thoughtfully plan procedures, consider ongoing nutritional status, and promote opportunities for patients to exert some control over their environment may diminish the burden of extrinsic stressors encountered by all patients in the hospital and improve outcomes after discharge.
Hospitals are increasingly recognizing the importance of improving patients’ experience of hospitalization by reducing exposure to potential toxicities. For example, many hospitals are now attempting to reduce sleep disturbances and sleep latency through reduced nighttime noise and light levels, fewer nighttime interruptions for vital signs checks and medication administration, and commonsensical interventions like massages, herbal teas, and warm milk prior to bedtime.89 Likewise, intensive care units are targeting environmental and physical stressors with a multifaceted approach to decrease sedative use, promote healthy sleep cycles, and encourage exercise and ambulation even in those patients who are mechanically ventilated.30 Another promising development has been the increase of Hospital at Home programs. In these programs, patients who meet the criteria for inpatient admission are instead comprehensively managed at home for their acute illness through a multidisciplinary effort between physicians, nurses, social workers, physical therapists, and others. Patients hospitalized at home report higher levels of satisfaction and have modest functional gains, improved health-related quality of life, and decreased risk of mortality at 6 months compared with hospitalized patients.90,91 With some admitting diagnoses (eg, heart failure), hospitalization at home may be associated with decreased readmission risk.92 Although not yet investigated on a physiologic level, perhaps the benefits of hospital at home are partially due to the dramatic difference in exposure to environmental stressors.
A tool that quantifies hospital-associated stress may help health providers appreciate the experience of patients and better target interventions to aspects of their structure and process that contribute to allostatic overload. Importantly, allostatic overload cannot be identified by one biomarker of stress but instead requires evidence of dysregulation across inflammatory, neuroendocrine, hormonal, and cardiometabolic systems. Future studies to address the burden of stress faced by hospitalized patients should consider a summative measure of multisystem dysregulation as opposed to isolated assessments of individual biomarkers. Allostatic load has previously been operationalized as the summation of a variety of hemodynamic, hormonal, and metabolic factors, including blood pressure, lipid profile, glycosylated hemoglobin, cortisol, catecholamine levels, and inflammatory markers.93 To develop a hospital-associated allostatic load index, models should ideally be adjusted for acute illness severity, patient-reported stress, and capacity for stress resilience. This tool could then be used to quantify hospitalization-related allostatic load and identify those at greatest risk for adverse events after discharge, as well as measure the effectiveness of strategic environmental interventions (Table 2). A natural first experiment may be a comparison of the allostatic load of hospitalized patients versus those hospitalized at home.
The risk of adverse outcomes after discharge is likely a function of the vulnerability of the patient and the degree to which the patient’s healthcare team and social support network mitigates this vulnerability. That is, there is a risk that a person struggles in the postdischarge period and, in many circumstances, a strong healthcare team and social network can identify health problems early and prevent them from progressing to the point that they require hospitalization.13,94-96 There are also hospital occurrences, outside of allostatic load, that can lead to complications that lengthen the stay, weaken the patient, and directly contribute to subsequent vulnerability.94,97 Our contention is that the allostatic load of hospitalization, which may also vary by patient depending on the circumstances of hospitalization, is just one contributor, albeit potentially an important one, to vulnerability to medical problems after discharge.
In conclusion, a plausible etiology of PHS is the maladaptive mind-body consequences of common stressors during hospitalization that compound the stress of acute illness and produce allostatic overload. This stress-induced dysfunction potentially contributes to a spectrum of generalized disease susceptibility and risk of adverse outcomes after discharge. Focused efforts to diminish patient exposure to hospital-related stressors during and after hospitalization might diminish the presence or severity of PHS. Viewing PHS from this perspective enables the development of hypothesis-driven risk-prediction models, encourages critical contemplation of traditional hospitalization, and suggests that targeted environmental interventions may significantly reduce adverse outcomes.
After discharge from the hospital, patients have a significantly elevated risk for adverse events, including emergency department use, hospital readmission, and death. More than 1 in 3 patients discharged from the hospital require acute care in the month after hospital discharge, and more than 1 in 6 require readmission, with readmission diagnoses frequently differing from those of the preceding hospitalization.1-4 This heightened susceptibility to adverse events persists beyond 30 days but levels off by 7 weeks after discharge, suggesting that the period of increased risk is transient and dynamic.5
The term posthospital syndrome (PHS) describes this period of vulnerability to major adverse events following hospitalization.6 In addition to increased risk for readmission and mortality, patients in this period often show evidence of generalized dysfunction with new cognitive impairment, mobility disability, or functional decline.7-12 To date, the etiology of this vulnerability is neither well understood nor effectively addressed by transitional care interventions.13
One hypothesis to explain PHS is that stressors associated with the experience of hospitalization contribute to transient multisystem dysfunction that induces susceptibility to a broad range of medical maladies. These stressors include frequent sleep disruption, noxious sounds, painful stimuli, mobility restrictions, and poor nutrition.12 The stress hypothesis as a cause of PHS is therefore based, in large part, on evidence about allostasis and the deleterious effects of allostatic overload.
Allostasis defines a system functioning within normal stress-response parameters to promote adaptation and survival.14 In allostasis, the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic and parasympathetic branches of the autonomic nervous system (ANS) exist in homeostatic balance and respond to environmental stimuli within a range of healthy physiologic parameters. The hallmark of a system in allostasis is the ability to rapidly activate, then successfully deactivate, a stress response once the stressor (ie, threat) has resolved.14,15 To promote survival and potentiate “fight or flight” mechanisms, an appropriate stress response necessarily impacts multiple physiologic systems that result in hemodynamic augmentation and gluconeogenesis to support the anticipated action of large muscle groups, heightened vigilance and memory capabilities to improve rapid decision-making, and enhancement of innate and adaptive immune capabilities to prepare for wound repair and infection defense.14-16 The stress response is subsequently terminated by negative feedback mechanisms of glucocorticoids as well as a shift of the ANS from sympathetic to parasympathetic tone.17,18
Extended or repetitive stress exposure, however, leads to dysregulation of allostatic mechanisms responsible for stress adaptation and hinders an efficient and effective stress response. After extended stress exposure, baseline (ie, resting) HPA activity resets, causing a disruption of normal diurnal cortisol rhythm and an increase in total cortisol concentration. Moreover, in response to stress, HPA and ANS system excitation becomes impaired, and negative feedback properties are undermined.14,15 This maladaptive state, known as allostatic overload, disrupts the finely tuned mechanisms that are the foundation of mind-body balance and yields pathophysiologic consequences to multiple organ systems. Downstream ramifications of allostatic overload include cognitive deterioration, cardiovascular and immune system dysfunction, and functional decline.14,15,19
Although a stress response is an expected and necessary aspect of acute illness that promotes survival, the central thesis of this work is that additional environmental and social stressors inherent in hospitalization may unnecessarily compound stress and increase the risk of HPA axis dysfunction, allostatic overload, and subsequent multisystem dysfunction, predisposing individuals to adverse outcomes after hospital discharge. Based on data from both human subjects and animal models, we present a possible pathophysiologic mechanism for the postdischarge vulnerability of PHS, encourage critical contemplation of traditional hospitalization, and suggest interventions that might improve outcomes.
POSTHOSPITAL SYNDROME
Posthospital syndrome (PHS) describes a transient period of vulnerability after hospitalization during which patients are at elevated risk for adverse events from a broad range of conditions. In support of this characterization, epidemiologic data have demonstrated high rates of adverse outcomes following hospitalization. For example, data have shown that more than 1 in 6 older adults is readmitted to the hospital within 30 days of discharge.20 Death is also common in this first month, during which rates of postdischarge mortality may exceed initial inpatient mortality.21,22 Elevated vulnerability after hospitalization is not restricted to older adults, as readmission risk among younger patients 18 to 64 years of age may be even higher for selected conditions, such as heart failure.3,23
Vulnerability after hospitalization is broad. In patients over age 65 initially admitted for heart failure or acute myocardial infarction, only 35% and 10% of readmissions are for recurrent heart failure or reinfarction, respectively.1 Nearly half of readmissions are for noncardiovascular causes.1 Similarly, following hospitalization for pneumonia, more than 60 percent of readmissions are for nonpulmonary etiologies. Moreover, the risk for all these causes of readmission is much higher than baseline risk, indicating an extended period of lack of resilience to many types of illness.24 These patterns of broad susceptibility also extend to younger adults hospitalized with common medical conditions.3
Accumulating evidence suggests that hospitalized patients face functional decline, debility, and risk for adverse events despite resolution of the presenting illness, implying perhaps that the hospital environment itself is hazardous to patients’ health. In 1993, Creditor hypothesized that the “hazards of hospitalization,” including enforced bed-rest, sensory deprivation, social isolation, and malnutrition lead to a “cascade of dependency” in which a collection of small insults to multiple organ systems precipitates loss of function and debility despite cure or resolution of presenting illness.12 Covinsky (2011) later defined hospitalization-associated disability as an iatrogenic hospital-related “disorder” characterized by new impairments in abilities to perform basic activities of daily living such as bathing, feeding, toileting, dressing, transferring, and walking at the time of hospital discharge.11 Others have described a postintensive-care syndrome (PICS),25 characterized by cognitive, psychiatric, and physical impairments acquired during hospitalization for critical illness that persist postdischarge and increase the long-term risk for adverse outcomes, including elevated mortality rates,26,27 readmission rates,28 and physical disabilities.29 Similar to the “hazards of hospitalization,” PICS is thought to be related to common experiences of ICU stays, including mobility restriction, sensory deprivation, sleep disruption, sedation, malnutrition, and polypharmacy.30-33
Taken together, these data suggest that adverse health consequences attributable to hospitalization extend across the spectrum of age, presenting disease severity, and hospital treatment location. As detailed below, the PHS hypothesis is rooted in a mechanistic understanding of the role of exogenous stressors in producing physiologic dysregulation and subsequent adverse health effects across multiple organ systems.
Nature of Stress in the Hospital
Compounding the stress of acute illness, hospitalized patients are routinely and repetitively exposed to a wide variety of environmental stressors that may have downstream adverse consequences (Table 1). In the absence of overt clinical manifestations of harm, the possible subclinical physiologic dysfunction generated by the following stress exposures may increase patients’ susceptibility to the manifestations of PHS.
Sleep Disruption
Sleep disruptions trigger potent stress responses,34,35 yet they are common occurrences during hospitalization. In surveys, about half of patients report poor sleep quality during hospitalization that persists for many months after discharge.36 In a simulated hospital setting, test subjects exposed to typical hospital sounds (paging system, machine alarms, etc.) experienced significant sleep-wake cycle abnormalities.37 Although no work has yet focused specifically on the physiologic consequences of sleep disruption and stress in hospitalized patients, in healthy humans, mild sleep disruption has clear effects on allostasis by disrupting HPA activity, raising cortisol levels, diminishing parasympathetic tone, and impairing cognitive performance.18,34,35,38,39
Malnourishment
Malnourishment in hospitalized patients is common, with one-fifth of hospitalized patients receiving nothing per mouth or clear liquid diets for more than 3 continuous days,40 and one-fifth of hospitalized elderly patients receiving less than half of their calculated nutrition requirements.41 Although the relationship between food restriction, cortisol levels, and postdischarge outcomes has not been fully explored, in healthy humans, meal anticipation, meal withdrawal (withholding an expected meal), and self-reported dietary restraint are known to generate stress responses.42,43 Furthermore, malnourishment during hospitalization is associated with increased 90-day and 1-year mortality after discharge,44 adding malnourishment to the list of plausible components of hospital-related stress.
Mobility Restriction
Physical activity counterbalances stress responses and minimizes downstream consequences of allostatic load,15 yet mobility limitations via physical and chemical restraints are common in hospitalized patients, particularly among the elderly.45-47 Many patients are tethered to devices that make ambulation hazardous, such as urinary catheters and infusion pumps. Even without physical or chemical restraints or a limited mobility order, patients may be hesitant to leave the room so as not to miss transport to a diagnostic study or an unscheduled physician’s visit. Indeed, mobility limitations of hospitalized patients increase the risk for adverse events after discharge, while interventions designed to encourage mobility are associated with improved postdischarge outcomes.47,48
Other Stressors
Other hospital-related aversive stimuli are less commonly quantified, but clearly exist. According to surveys of hospitalized patients, sources of emotional stress include social isolation; loss of autonomy and privacy; fear of serious illness; lack of control over activities of daily living; lack of clear communication between treatment team and patients; and death of a patient roommate.49,50 Furthermore, consider the physical discomfort and emotional distress of patients with urinary incontinence awaiting assistance for a diaper or bedding change or the pain of repetitive blood draws or other invasive testing. Although individualized, the subjective discomfort and emotional distress associated with these experiences undoubtedly contribute to the stress of hospitalization.
IMPACT OF ALLOSTATIC OVERLOAD ON PHYSIOLOGIC FUNCTION
Animal Models of Stress
Laboratory techniques reminiscent of the numerous environmental stressors associated with hospitalization have been used to reliably trigger allostatic overload in healthy young animals.51 These techniques include sequential exposure to aversive stimuli, including food and water deprivation, continuous overnight illumination, paired housing with known and unknown cagemates, mobility restriction, soiled cage conditions, and continuous noise. All of these techniques have been shown to cause HPA axis and ANS dysfunction, allostatic overload, and subsequent stress-mediated consequences to multiple organ systems.19,52-54 Given the remarkable similarity of these protocols to common experiences during hospitalization, animal models of stress may be useful in understanding the spectrum of maladaptive consequences experienced by patients within the hospital (Figure 1).
These animal models of stress have resulted in a number of instructive findings. For example, in rodents, extended stress exposure induces structural and functional remodeling of neuronal networks that precipitate learning and memory, working memory, and attention impairments.55-57 These exposures also result in cardiovascular abnormalities, including dyslipidemia, progressive atherosclerosis,58,59 and enhanced inflammatory cytokine expression,60 all of which increase both atherosclerotic burden and susceptibility to plaque rupture, leading to elevated risk for major cardiovascular adverse events. Moreover, these extended stress exposures in animals increase susceptibility to both bacterial and viral infections and increase their severity.16,61 This outcome appears to be driven by a stress-induced elevation of glucocorticoid levels, decreased leukocyte proliferation, altered leukocyte trafficking, and a transition to a proinflammatory cytokine environment.16, 61 Allostatic overload has also been shown to contribute to metabolic dysregulation involving insulin resistance, persistence of hyperglycemia, dyslipidemia, catabolism of lean muscle, and visceral adipose tissue deposition.62-64 In addition to cardiovascular, immune, and metabolic consequences of allostatic overload, the spectrum of physiologic dysfunction in animal models is broad and includes mood disorder symptoms,65 intestinal barrier abnormalities,66 airway reactivity exacerbation,67 and enhanced tumor growth.68
Although the majority of this research highlights the multisystem effects of variable stress exposure in healthy animals, preliminary evidence suggests that aged or diseased animals subjected to additional stressors display a heightened inflammatory cytokine response that contributes to exaggerated sickness behavior and greater and prolonged cognitive deficits.69 Future studies exploring the consequences of extended stress exposure in animals with existing disease or debility may therefore more closely simulate the experience of hospitalized patients and perhaps further our understanding of PHS.
Hospitalized Patients
While no intervention studies have examined the effects of potential hospital stressors on the development of allostatic overload, there is evidence from small studies that dysregulated stress responses during hospitalization are associated with adverse events. For example, high serum cortisol, catecholamine, and proinflammatory cytokine levels during hospitalization have individually been associated with the development of cognitive dysfunction,70-72 increased risk of cardiovascular events such as myocardial infarction and stroke in the year following discharge,73-76 and the development of wound infections after discharge.77 Moreover, elevated plasma glucose during admission for myocardial infarction in patients with or without diabetes has been associated with greater in-hospital and 1-year mortality,78 with a similar relationship seen between elevated plasma glucose and survival after admission for stroke79 and pneumonia.80 Furthermore, in addition to atherothrombosis, stress may contribute to the risk for venous thromboembolism,81 resulting in readmissions for deep vein thrombosis or pulmonary embolism posthospitalization. Although potentially surrogate markers of illness acuity, a handful of studies have shown that these stress biomarkers are actually only weakly correlated with,82 or independent of,72,76 disease severity. As discussed in detail below, future studies utilizing a summative measure of multisystem physiologic dysfunction as opposed to individual biomarkers may more accurately reflect the cumulative stress effects of hospitalization and subsequent risk for adverse events.
Additional Considerations
Elderly patients, in particular, may have heightened susceptibility to the consequences of allostatic overload due to common geriatric issues such as multimorbidity and frailty. Patients with chronic diseases display both baseline HPA axis abnormalities as well as dysregulated stress responses and may therefore be more vulnerable to hospitalization-related stress. For example, when subjected to psychosocial stress, patients with chronic conditions such as diabetes, heart failure, or atherosclerosis demonstrate elevated cortisol levels, increased circulating markers of inflammation, as well as prolonged hemodynamic recovery after stress resolution compared with normal controls.83-85 Additionally, frailty may affect an individual’s susceptibility to exogenous stress. Indeed, frailty identified on hospital admission increases the risk for adverse outcomes during hospitalization and postdischarge.86 Although the specific etiology of this relationship is unclear, persons with frailty are known to have elevated levels of cortisol and other inflammatory markers,87,88 which may contribute to adverse outcomes in the face of additional stressors.
IMPLICATIONS AND NEXT STEPS
A large body of evidence stretching from bench to bedside suggests that environmental stressors associated with hospitalization are toxic. Understanding PHS within the context of hospital-induced allostatic overload presents a unifying theory for the interrelated multisystem dysfunction and increased susceptibility to adverse events that patients experience after discharge (Figure 2). Furthermore, it defines a potential pathophysiological mechanism for the cognitive impairment, elevated cardiovascular risk, immune system dysfunction, metabolic derangements, and functional decline associated with PHS. Additionally, this theory highlights environmental interventions to limit PHS development and suggests mechanisms to promote stress resilience. Although it is difficult to disentangle the consequences of the endogenous stress triggered by an acute illness from the exogenous stressors related to hospitalization, it is likely that the 2 simultaneous exposures compound risk for stress system dysregulation and allostatic overload. Moreover, hospitalized patients with preexisting HPA axis dysfunction at baseline from chronic disease or advancing age may be even more susceptible to these adverse outcomes. If this hypothesis is true, a reduction in PHS would require mitigation of the modifiable environmental stressors encountered by patients during hospitalization. Directed efforts to diminish ambient noise, limit nighttime disruptions, thoughtfully plan procedures, consider ongoing nutritional status, and promote opportunities for patients to exert some control over their environment may diminish the burden of extrinsic stressors encountered by all patients in the hospital and improve outcomes after discharge.
Hospitals are increasingly recognizing the importance of improving patients’ experience of hospitalization by reducing exposure to potential toxicities. For example, many hospitals are now attempting to reduce sleep disturbances and sleep latency through reduced nighttime noise and light levels, fewer nighttime interruptions for vital signs checks and medication administration, and commonsensical interventions like massages, herbal teas, and warm milk prior to bedtime.89 Likewise, intensive care units are targeting environmental and physical stressors with a multifaceted approach to decrease sedative use, promote healthy sleep cycles, and encourage exercise and ambulation even in those patients who are mechanically ventilated.30 Another promising development has been the increase of Hospital at Home programs. In these programs, patients who meet the criteria for inpatient admission are instead comprehensively managed at home for their acute illness through a multidisciplinary effort between physicians, nurses, social workers, physical therapists, and others. Patients hospitalized at home report higher levels of satisfaction and have modest functional gains, improved health-related quality of life, and decreased risk of mortality at 6 months compared with hospitalized patients.90,91 With some admitting diagnoses (eg, heart failure), hospitalization at home may be associated with decreased readmission risk.92 Although not yet investigated on a physiologic level, perhaps the benefits of hospital at home are partially due to the dramatic difference in exposure to environmental stressors.
A tool that quantifies hospital-associated stress may help health providers appreciate the experience of patients and better target interventions to aspects of their structure and process that contribute to allostatic overload. Importantly, allostatic overload cannot be identified by one biomarker of stress but instead requires evidence of dysregulation across inflammatory, neuroendocrine, hormonal, and cardiometabolic systems. Future studies to address the burden of stress faced by hospitalized patients should consider a summative measure of multisystem dysregulation as opposed to isolated assessments of individual biomarkers. Allostatic load has previously been operationalized as the summation of a variety of hemodynamic, hormonal, and metabolic factors, including blood pressure, lipid profile, glycosylated hemoglobin, cortisol, catecholamine levels, and inflammatory markers.93 To develop a hospital-associated allostatic load index, models should ideally be adjusted for acute illness severity, patient-reported stress, and capacity for stress resilience. This tool could then be used to quantify hospitalization-related allostatic load and identify those at greatest risk for adverse events after discharge, as well as measure the effectiveness of strategic environmental interventions (Table 2). A natural first experiment may be a comparison of the allostatic load of hospitalized patients versus those hospitalized at home.
The risk of adverse outcomes after discharge is likely a function of the vulnerability of the patient and the degree to which the patient’s healthcare team and social support network mitigates this vulnerability. That is, there is a risk that a person struggles in the postdischarge period and, in many circumstances, a strong healthcare team and social network can identify health problems early and prevent them from progressing to the point that they require hospitalization.13,94-96 There are also hospital occurrences, outside of allostatic load, that can lead to complications that lengthen the stay, weaken the patient, and directly contribute to subsequent vulnerability.94,97 Our contention is that the allostatic load of hospitalization, which may also vary by patient depending on the circumstances of hospitalization, is just one contributor, albeit potentially an important one, to vulnerability to medical problems after discharge.
In conclusion, a plausible etiology of PHS is the maladaptive mind-body consequences of common stressors during hospitalization that compound the stress of acute illness and produce allostatic overload. This stress-induced dysfunction potentially contributes to a spectrum of generalized disease susceptibility and risk of adverse outcomes after discharge. Focused efforts to diminish patient exposure to hospital-related stressors during and after hospitalization might diminish the presence or severity of PHS. Viewing PHS from this perspective enables the development of hypothesis-driven risk-prediction models, encourages critical contemplation of traditional hospitalization, and suggests that targeted environmental interventions may significantly reduce adverse outcomes.
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67. Joachim RA, Quarcoo D, Arck PC, Herz U, Renz H, Klapp BF. Stress enhances airway reactivity and airway inflammation in an animal model of allergic bronchial asthma. Psychosom Med. 2003;65(5):811-815. http://dx.doi.org/10.1097/01.PSY.0000088582.50468.A3.
68. Thaker PH, Han LY, Kamat AA, et al. Chronic stress promotes tumor growth and angiogenesis in a mouse model of ovarian carcinoma. Nat Med. 2006;12(8):939-944. http://dx.doi.org/10.1038/nm1447.
69. Schreuder L, Eggen BJ, Biber K, Schoemaker RG, Laman JD, de Rooij SE. Pathophysiological and behavioral effects of systemic inflammation in aged and diseased rodents with relevance to delirium: A systematic review. Brain Behav Immun. 2017;62:362-381. http://dx.doi.org/10.1016/j.bbi.2017.01.010.
70. Mu DL, Li LH, Wang DX, et al. High postoperative serum cortisol level is associated with increased risk of cognitive dysfunction early after coronary artery bypass graft surgery: a prospective cohort study. PLoS One. 2013;8(10):e77637. http://dx.doi.org/10.1371/journal.pone.0077637.
71. Mu DL, Wang DX, Li LH, et al. High serum cortisol level is associated with increased risk of delirium after coronary artery bypass graft surgery: a prospective cohort study. Crit Care. 2010;14(6):R238. http://dx.doi.org/10.1186/cc9393.
72. Nguyen DN, Huyghens L, Zhang H, Schiettecatte J, Smitz J, Vincent JL. Cortisol is an associated-risk factor of brain dysfunction in patients with severe sepsis and septic shock. Biomed Res Int. 2014;2014:712742. http://dx.doi.org/10.1155/2014/712742.
73. Elkind MS, Carty CL, O’Meara ES, et al. Hospitalization for infection and risk of acute ischemic stroke: the Cardiovascular Health Study. Stroke. 2011;42(7):1851-1856. http://dx.doi.org/10.1161/STROKEAHA.110.608588.
74. Feibel JH, Hardy PM, Campbell RG, Goldstein MN, Joynt RJ. Prognostic value of the stress response following stroke. JAMA. 1977;238(13):1374-1376.
75. Jutla SK, Yuyun MF, Quinn PA, Ng LL. Plasma cortisol and prognosis of patients with acute myocardial infarction. J Cardiovasc Med (Hagerstown). 2014;15(1):33-41. http://dx.doi.org/10.2459/JCM.0b013e328364100b.
76. Yende S, D’Angelo G, Kellum JA, et al. Inflammatory markers at hospital discharge predict subsequent mortality after pneumonia and sepsis. Am J Respir Crit Care Med. 2008;177(11):1242-1247. http://dx.doi.org/10.1164/rccm.200712-1777OC.
77. Gouin JP, Kiecolt-Glaser JK. The impact of psychological stress on wound healing: methods and mechanisms. Immunol Allergy Clin North Am. 2011;31(1):81-93. http://dx.doi.org/10.1016/j.iac.2010.09.010.
78. Capes SE, Hunt D, Malmberg K, Gerstein HC. Stress hyperglycaemia and increased risk of death after myocardial infarction in patients with and without diabetes: a systematic overview. Lancet. 2000;355(9206):773-778. http://dx.doi.org/10.1016/S0140-6736(99)08415-9.
79. O’Neill PA, Davies I, Fullerton KJ, Bennett D. Stress hormone and blood glucose response following acute stroke in the elderly. Stroke. 1991;22(7):842-847. http://dx.doi.org/10.1161/01.STR.22.7.842.
80. Waterer GW, Kessler LA, Wunderink RG. Medium-term survival after hospitalization with community-acquired pneumonia. Am J Respir Crit Care Med. 2004;169(8):910-914. http://dx.doi.org/10.1164/rccm.200310-1448OC.
81. Rosengren A, Freden M, Hansson PO, Wilhelmsen L, Wedel H, Eriksson H. Psychosocial factors and venous thromboembolism: a long-term follow-up study of Swedish men. J Thrombosis Haemostasis. 2008;6(4):558-564. http://dx.doi.org/10.1111/j.1538-7836.2007.02857.x.
82. Oswald GA, Smith CC, Betteridge DJ, Yudkin JS. Determinants and importance of stress hyperglycaemia in non-diabetic patients with myocardial infarction. BMJ. 1986;293(6552):917-922. http://dx.doi.org/10.1136/bmj.293.6552.917.
83. Middlekauff HR, Nguyen AH, Negrao CE, et al. Impact of acute mental stress on sympathetic nerve activity and regional blood flow in advanced heart failure: implications for ‘triggering’ adverse cardiac events. Circulation. 1997;96(6):1835-1842. http://dx.doi.org/10.1161/01.CIR.96.6.1835.
84. Nijm J, Jonasson L. Inflammation and cortisol response in coronary artery disease. Ann Med. 2009;41(3):224-233. http://dx.doi.org/10.1080/07853890802508934.
85. Steptoe A, Hackett RA, Lazzarino AI, et al. Disruption of multisystem responses to stress in type 2 diabetes: investigating the dynamics of allostatic load. Proc Natl Acad Sci U S A. 2014;111(44):15693-15698. http://dx.doi.org/10.1073/pnas.1410401111.
86. Sepehri A, Beggs T, Hassan A, et al. The impact of frailty on outcomes after cardiac surgery: a systematic review. J Thorac Cardiovasc Surg. 2014;148(6):3110-3117. http://dx.doi.org/10.1016/j.jtcvs.2014.07.087.
87. Johar H, Emeny RT, Bidlingmaier M, et al. Blunted diurnal cortisol pattern is associated with frailty: a cross-sectional study of 745 participants aged 65 to 90 years. J Clin Endocrinol Metab. 2014;99(3):E464-468. http://dx.doi.org/10.1210/jc.2013-3079.
88. Yao X, Li H, Leng SX. Inflammation and immune system alterations in frailty. Clin Geriatr Med. 2011;27(1):79-87. http://dx.doi.org/10.1016/j.cger.2010.08.002.
89. Hospital Elder Life Program (HELP) for Prevention of Delirium. 2017; http://www.hospitalelderlifeprogram.org/. Accessed February 16, 2018.
90. Shepperd S, Doll H, Angus RM, et al. Admission avoidance hospital at home. Cochrane Database of System Rev. 2008;(4):CD007491. http://dx.doi.org/10.1002/14651858.CD007491.pub2’
91. Leff B, Burton L, Mader SL, et al. Comparison of functional outcomes associated with hospital at home care and traditional acute hospital care. J Am Geriatrics Soc. 2009;57(2):273-278. http://dx.doi.org/10.1111/j.1532-5415.2008.02103.x.
92. Qaddoura A, Yazdan-Ashoori P, Kabali C, et al. Efficacy of hospital at home in patients with heart failure: a systematic review and meta-analysis. PloS One. 2015;10(6):e0129282. http://dx.doi.org/10.1371/journal.pone.0129282.
93. Seeman T, Gruenewald T, Karlamangla A, et al. Modeling multisystem biological risk in young adults: The Coronary Artery Risk Development in Young Adults Study. Am J Hum Biol. 2010;22(4):463-472. http://dx.doi.org/10.1002/ajhb.21018.
94. Auerbach AD, Kripalani S, Vasilevskis EE, et al. Preventability and causes of readmissions in a national cohort of general medicine patients. JAMA Intern Med. 2016;176(4):484-493. http://dx.doi.org/10.1001/jamainternmed.2015.7863.
95. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520-528. http://dx.doi.org/10.7326/0003-4819-155-8-201110180-00008.
96. Takahashi PY, Naessens JM, Peterson SM, et al. Short-term and long-term effectiveness of a post-hospital care transitions program in an older, medically complex population. Healthcare. 2016;4(1):30-35. http://dx.doi.org/10.1016/j.hjdsi.2015.06.006.
97. Dharmarajan K, Swami S, Gou RY, Jones RN, Inouye SK. Pathway from delirium to death: potential in-hospital mediators of excess mortality. J Am Geriatr Soc. 2017;65(5):1026-1033. http://dx.doi.org/10.1111/jgs.14743.
© 2018 Society of Hospital Medicine
Risk After Hospitalization
The immediate period after hospital discharge is dangerous. Patients' health, often marginal at best, frequently deteriorates, sending them to the emergency department,[1] back to the hospital inpatient service,[2] or into a period of functional decline.[3, 4] Among older patients hospitalized with heart failure, for example, death is even more common in the month following discharge than during the initial hospital stay.[5, 6] Vulnerabilities in this period are many, and patients are susceptible to deterioration in health from a broad spectrum of conditions, not just the initial illness that triggered hospitalization.[7] This period has been labeled posthospital syndrome, as it appears that patients have an acquired, transient period of generalized risk to a wide range of medical problems.[8] As recognition of these risks has increased, the goal of improved short‐term outcomes after hospitalization has become a focus for providers, payers, and policymakers.[9]
In this issue of the Journal of Hospital Medicine, McAlister and colleagues10 ask whether short‐term vulnerability after hospitalization is related to weekend versus weekday discharge. After examining almost 8000 patients discharged from the general medical wards of 7 teaching hospitals in Alberta, Canada, the authors found that only 1 in 7 were discharged on weekends, defined as Saturday or Sunday. Patients discharged on the weekend were younger, had fewer chronic health conditions, and shorter average lengths of stay. In analyses adjusted for patient demographics and a measure of short‐term risk after hospitalization (LACE score [length of hospital stay, acuity of admission, comorbidity burden quantified using the Charlson Comorbidity Index, and emergency department visits in the 6 months prior to admission]), weekend discharge was not associated with higher rates of unplanned readmission or death at 30 days.
Most strikingly, only the healthiest patients were discharged on weekends. These results are similar to findings from the authors' previous work on patients hospitalized with heart failure.[11] Yet the implications for discharge planning are much less clear, as the few analyses of discharge day from the authors[11] and others[12] do not account for the range of factors that may influence risk after hospitalization such as patients' clinical characteristics, the quality of both hospital and transitional care, and the posthospital environments to which patients are discharged. Not surprisingly, different methodological approaches have shown weekend discharge to be associated with a range of outcomes including lower,[12] identical,[10] and higher[11] rates of unplanned readmission and death. Moreover, the influence of discharge timing itself is likely to involve further complexities including patients' readiness for discharge,[13] the specific days of the week on which both admission and discharge occur,[14] and the outpatient resources made available to patients by specific health insurance carriers.[14]
These studies illustrate a fundamental issue with our efforts to reduce short‐term readmission, namely, that we do not understand which factors most influence risk.[15] Prediction models have generally focused on traditional markers of risk including patients' demographic characteristics, their physical examination findings, and laboratory test results. Although models based on these variables are often excellent at discriminating between patients who are likely to die soon after hospitalization, their ability to identify specific patients who will be rehospitalized has been mediocre.[16, 17] This difficulty with prediction suggests that readmission has far more complex determinants than death in the short‐term period after hospitalization. Unfortunately, we have yet to identify and model the factors that matter most.
Where should we look to find these additional sources of vulnerability after hospitalization? Previous research has made clear that we are unlikely to find single markers of risk that adequately predict the future. Rather, we will need to develop more complete understandings of patients including their dynamics of recovery, the role of the hospital environment in prolonging or instigating further vulnerability, the manners by which organizational context and implementation strategies impact transitional care, and the ways in which social and environmental factors hasten or retard recovery. For each of these categories, there are multiple specific questions to address. The following are illustrative examples.
PATIENT FACTORS
What is the role of multiple chronic conditions in risk after discharge? Are specific clusters of chronic diseases particularly correlated with adverse health events? Moreover, how do common impairments and syndromes in older persons, such as cognitive impairment, functional impairment, difficulty with walking, sleep disturbance, and frailty, contribute to posthospitalization vulnerability? Would measurements of mobility and function immediately after discharge provide additional value in risk stratification beyond such measurements made during hospitalization?
HOSPITAL ENVIRONMENT
How does ambient sound, ambient light, shared rooms, and frequent awakening for vital signs checks, diagnostic tests, or medication administration affect sleep duration and quality, incident delirium, and in‐hospital complications? What influence do these factors have on postdischarge recovery of baseline sleep patterns and cognition? How does forced immobility from bed rest or restraints influence recovery of muscle mass and the function of arms and legs after discharge? How does fasting prior to diagnostic tests or therapeutic interventions impact recovery of weight, recovery of strength, and susceptibility to further illnesses after hospitalization?
CARE TRANSITIONS
What are the influences of organizational context on the success or failure of specific transitional care interventions? What is the relative importance of senior managerial commitment to improving postdischarge outcomes, the presence of local champions for quality, and an organization's culture of learning, collaboration, and belief in shared accountability? How does the particular way in which a program is implemented and managed with regard to its staffing, education of key personnel, available resources, methods for data collection, measurement of results, and approach to continuous quality improvement relate to its ability to reduce readmission?
SOCIAL AND ENVIRONMENTAL FACTORS
What particular types of emotional, informational, and instrumental supports are most critical after hospitalization to avoid subsequent adverse health events? How do financial issues contribute to difficulties with follow‐up care and medication management, adherence to dietary and activity recommendations, and levels of stress and anxiety following discharge? How does the home environment mitigate or exacerbate new vulnerabilities after hospitalization?
Ultimately, an improved understanding of the breadth of factors that predict recurrent medical illness after discharge, as signaled by readmission, and the manner in which they confer risk will improve both risk prediction and efforts to mitigate vulnerability after hospitalization. Ultimately, we need to learn how to align our hospital environments, transitional care interventions, and strategies for longitudinal engagement in ways that improve patients' recovery. The work by McAlister and colleagues[10] is a step in the right direction, as it breaks with the exclusive examination of traditional patient factors to incorporate complexities associated with discharge timing. Such investigations are necessary to truly understand the myriad sources of risk and recovery after hospital discharge.
ACKNOWLEDGMENTS
Disclosures: Dr. Dharmarajan is supported by grant K23AG048331‐01 from the National Institute on Aging and the American Federation for Aging Research through the Paul B. Beeson Career Development Award Program. Dr. Krumholz is supported by grant 1U01HL105270‐05 (Center for Cardiovascular Outcomes Research at Yale University) from the National Heart, Lung, and Blood Institute. The content is solely the responsibility of the authors and does not represent the official views of the National Institute on Aging; National Heart, Lung, and Blood Institute; or American Federation for Aging Research. Drs. Dharmarajan and Krumholz work under contract with the Centers for Medicare & Medicaid Services to develop and maintain performance measures. Dr. Krumholz is the chair of a cardiac scientific advisory board for UnitedHealth and is the recipient of research grants from Medtronic and from Johnson & Johnson, through Yale University, to develop methods of clinical trial data sharing.
- , , , et al. Use of hospital‐based acute care among patients recently discharged from the hospital. JAMA. 2013;309:364–371.
- , , . Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360:1418–1428.
- , , , . Hospitalization, restricted activity, and the development of disability among older persons. JAMA. 2004;292:2115–2124.
- , , , . Change in disability after hospitalization or restricted activity in older persons. JAMA. 2010;304:1919–1928.
- , , , et al. Trends in length of stay and short‐term outcomes among Medicare patients hospitalized for heart failure, 1993–2006. JAMA. 2010;303:2141–2147.
- , , , et al. Comparison of hospital risk‐standardized mortality rates calculated by using in‐hospital and 30‐day models: an observational study with implications for hospital profiling. Ann Intern Med. 2012;156:19–26.
- , , , et al. Diagnoses and timing of 30‐day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia. JAMA. 2013;309:355–363.
- . Post‐hospital syndrome—an acquired, transient condition of generalized risk. N Engl J Med. 2013;368:100–102.
- , . Hospital readmissions and the Affordable Care Act: paying for coordinated quality care. JAMA. 2011;306:1794–1795.
- , , , . Post‐discharge outcomes are similar for weekend versus weekday discharges for general internal medicine patients admitted to teaching hospitals. J Hosp Med. 2015;10(2):69–74.
- , , , , . Postdischarge outcomes in heart failure are better for teaching hospitals and weekday discharges. Circ Heart Fail. 2013;6:922–929.
- , . Risk of death or readmission among people discharged from hospital on Fridays. CMAJ. 2002;166:1672–1673.
- , , , et al. Predictors of short‐term rehospitalization following discharge of patients hospitalized with community‐acquired pneumonia. Chest. 2009;136:1079–1085.
- , , . Should hospitals keep their patients longer? The role of inpatient and outpatient care in reducing readmissions. NBER working paper no. 20499. Cambridge, MA: National Bureau of Economic Research; 2014.
- , , , et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306:1688–1698.
- , . Strategies to reduce 30‐day readmissions in older patients hospitalized with heart failure and acute myocardial infarction. Curr Geri Rep. 2014;3:306–315.
- , , . Postdischarge environment following heart failure hospitalization: expanding the view of hospital readmission. J Am Heart Assoc. 2013;2:e000116.
The immediate period after hospital discharge is dangerous. Patients' health, often marginal at best, frequently deteriorates, sending them to the emergency department,[1] back to the hospital inpatient service,[2] or into a period of functional decline.[3, 4] Among older patients hospitalized with heart failure, for example, death is even more common in the month following discharge than during the initial hospital stay.[5, 6] Vulnerabilities in this period are many, and patients are susceptible to deterioration in health from a broad spectrum of conditions, not just the initial illness that triggered hospitalization.[7] This period has been labeled posthospital syndrome, as it appears that patients have an acquired, transient period of generalized risk to a wide range of medical problems.[8] As recognition of these risks has increased, the goal of improved short‐term outcomes after hospitalization has become a focus for providers, payers, and policymakers.[9]
In this issue of the Journal of Hospital Medicine, McAlister and colleagues10 ask whether short‐term vulnerability after hospitalization is related to weekend versus weekday discharge. After examining almost 8000 patients discharged from the general medical wards of 7 teaching hospitals in Alberta, Canada, the authors found that only 1 in 7 were discharged on weekends, defined as Saturday or Sunday. Patients discharged on the weekend were younger, had fewer chronic health conditions, and shorter average lengths of stay. In analyses adjusted for patient demographics and a measure of short‐term risk after hospitalization (LACE score [length of hospital stay, acuity of admission, comorbidity burden quantified using the Charlson Comorbidity Index, and emergency department visits in the 6 months prior to admission]), weekend discharge was not associated with higher rates of unplanned readmission or death at 30 days.
Most strikingly, only the healthiest patients were discharged on weekends. These results are similar to findings from the authors' previous work on patients hospitalized with heart failure.[11] Yet the implications for discharge planning are much less clear, as the few analyses of discharge day from the authors[11] and others[12] do not account for the range of factors that may influence risk after hospitalization such as patients' clinical characteristics, the quality of both hospital and transitional care, and the posthospital environments to which patients are discharged. Not surprisingly, different methodological approaches have shown weekend discharge to be associated with a range of outcomes including lower,[12] identical,[10] and higher[11] rates of unplanned readmission and death. Moreover, the influence of discharge timing itself is likely to involve further complexities including patients' readiness for discharge,[13] the specific days of the week on which both admission and discharge occur,[14] and the outpatient resources made available to patients by specific health insurance carriers.[14]
These studies illustrate a fundamental issue with our efforts to reduce short‐term readmission, namely, that we do not understand which factors most influence risk.[15] Prediction models have generally focused on traditional markers of risk including patients' demographic characteristics, their physical examination findings, and laboratory test results. Although models based on these variables are often excellent at discriminating between patients who are likely to die soon after hospitalization, their ability to identify specific patients who will be rehospitalized has been mediocre.[16, 17] This difficulty with prediction suggests that readmission has far more complex determinants than death in the short‐term period after hospitalization. Unfortunately, we have yet to identify and model the factors that matter most.
Where should we look to find these additional sources of vulnerability after hospitalization? Previous research has made clear that we are unlikely to find single markers of risk that adequately predict the future. Rather, we will need to develop more complete understandings of patients including their dynamics of recovery, the role of the hospital environment in prolonging or instigating further vulnerability, the manners by which organizational context and implementation strategies impact transitional care, and the ways in which social and environmental factors hasten or retard recovery. For each of these categories, there are multiple specific questions to address. The following are illustrative examples.
PATIENT FACTORS
What is the role of multiple chronic conditions in risk after discharge? Are specific clusters of chronic diseases particularly correlated with adverse health events? Moreover, how do common impairments and syndromes in older persons, such as cognitive impairment, functional impairment, difficulty with walking, sleep disturbance, and frailty, contribute to posthospitalization vulnerability? Would measurements of mobility and function immediately after discharge provide additional value in risk stratification beyond such measurements made during hospitalization?
HOSPITAL ENVIRONMENT
How does ambient sound, ambient light, shared rooms, and frequent awakening for vital signs checks, diagnostic tests, or medication administration affect sleep duration and quality, incident delirium, and in‐hospital complications? What influence do these factors have on postdischarge recovery of baseline sleep patterns and cognition? How does forced immobility from bed rest or restraints influence recovery of muscle mass and the function of arms and legs after discharge? How does fasting prior to diagnostic tests or therapeutic interventions impact recovery of weight, recovery of strength, and susceptibility to further illnesses after hospitalization?
CARE TRANSITIONS
What are the influences of organizational context on the success or failure of specific transitional care interventions? What is the relative importance of senior managerial commitment to improving postdischarge outcomes, the presence of local champions for quality, and an organization's culture of learning, collaboration, and belief in shared accountability? How does the particular way in which a program is implemented and managed with regard to its staffing, education of key personnel, available resources, methods for data collection, measurement of results, and approach to continuous quality improvement relate to its ability to reduce readmission?
SOCIAL AND ENVIRONMENTAL FACTORS
What particular types of emotional, informational, and instrumental supports are most critical after hospitalization to avoid subsequent adverse health events? How do financial issues contribute to difficulties with follow‐up care and medication management, adherence to dietary and activity recommendations, and levels of stress and anxiety following discharge? How does the home environment mitigate or exacerbate new vulnerabilities after hospitalization?
Ultimately, an improved understanding of the breadth of factors that predict recurrent medical illness after discharge, as signaled by readmission, and the manner in which they confer risk will improve both risk prediction and efforts to mitigate vulnerability after hospitalization. Ultimately, we need to learn how to align our hospital environments, transitional care interventions, and strategies for longitudinal engagement in ways that improve patients' recovery. The work by McAlister and colleagues[10] is a step in the right direction, as it breaks with the exclusive examination of traditional patient factors to incorporate complexities associated with discharge timing. Such investigations are necessary to truly understand the myriad sources of risk and recovery after hospital discharge.
ACKNOWLEDGMENTS
Disclosures: Dr. Dharmarajan is supported by grant K23AG048331‐01 from the National Institute on Aging and the American Federation for Aging Research through the Paul B. Beeson Career Development Award Program. Dr. Krumholz is supported by grant 1U01HL105270‐05 (Center for Cardiovascular Outcomes Research at Yale University) from the National Heart, Lung, and Blood Institute. The content is solely the responsibility of the authors and does not represent the official views of the National Institute on Aging; National Heart, Lung, and Blood Institute; or American Federation for Aging Research. Drs. Dharmarajan and Krumholz work under contract with the Centers for Medicare & Medicaid Services to develop and maintain performance measures. Dr. Krumholz is the chair of a cardiac scientific advisory board for UnitedHealth and is the recipient of research grants from Medtronic and from Johnson & Johnson, through Yale University, to develop methods of clinical trial data sharing.
The immediate period after hospital discharge is dangerous. Patients' health, often marginal at best, frequently deteriorates, sending them to the emergency department,[1] back to the hospital inpatient service,[2] or into a period of functional decline.[3, 4] Among older patients hospitalized with heart failure, for example, death is even more common in the month following discharge than during the initial hospital stay.[5, 6] Vulnerabilities in this period are many, and patients are susceptible to deterioration in health from a broad spectrum of conditions, not just the initial illness that triggered hospitalization.[7] This period has been labeled posthospital syndrome, as it appears that patients have an acquired, transient period of generalized risk to a wide range of medical problems.[8] As recognition of these risks has increased, the goal of improved short‐term outcomes after hospitalization has become a focus for providers, payers, and policymakers.[9]
In this issue of the Journal of Hospital Medicine, McAlister and colleagues10 ask whether short‐term vulnerability after hospitalization is related to weekend versus weekday discharge. After examining almost 8000 patients discharged from the general medical wards of 7 teaching hospitals in Alberta, Canada, the authors found that only 1 in 7 were discharged on weekends, defined as Saturday or Sunday. Patients discharged on the weekend were younger, had fewer chronic health conditions, and shorter average lengths of stay. In analyses adjusted for patient demographics and a measure of short‐term risk after hospitalization (LACE score [length of hospital stay, acuity of admission, comorbidity burden quantified using the Charlson Comorbidity Index, and emergency department visits in the 6 months prior to admission]), weekend discharge was not associated with higher rates of unplanned readmission or death at 30 days.
Most strikingly, only the healthiest patients were discharged on weekends. These results are similar to findings from the authors' previous work on patients hospitalized with heart failure.[11] Yet the implications for discharge planning are much less clear, as the few analyses of discharge day from the authors[11] and others[12] do not account for the range of factors that may influence risk after hospitalization such as patients' clinical characteristics, the quality of both hospital and transitional care, and the posthospital environments to which patients are discharged. Not surprisingly, different methodological approaches have shown weekend discharge to be associated with a range of outcomes including lower,[12] identical,[10] and higher[11] rates of unplanned readmission and death. Moreover, the influence of discharge timing itself is likely to involve further complexities including patients' readiness for discharge,[13] the specific days of the week on which both admission and discharge occur,[14] and the outpatient resources made available to patients by specific health insurance carriers.[14]
These studies illustrate a fundamental issue with our efforts to reduce short‐term readmission, namely, that we do not understand which factors most influence risk.[15] Prediction models have generally focused on traditional markers of risk including patients' demographic characteristics, their physical examination findings, and laboratory test results. Although models based on these variables are often excellent at discriminating between patients who are likely to die soon after hospitalization, their ability to identify specific patients who will be rehospitalized has been mediocre.[16, 17] This difficulty with prediction suggests that readmission has far more complex determinants than death in the short‐term period after hospitalization. Unfortunately, we have yet to identify and model the factors that matter most.
Where should we look to find these additional sources of vulnerability after hospitalization? Previous research has made clear that we are unlikely to find single markers of risk that adequately predict the future. Rather, we will need to develop more complete understandings of patients including their dynamics of recovery, the role of the hospital environment in prolonging or instigating further vulnerability, the manners by which organizational context and implementation strategies impact transitional care, and the ways in which social and environmental factors hasten or retard recovery. For each of these categories, there are multiple specific questions to address. The following are illustrative examples.
PATIENT FACTORS
What is the role of multiple chronic conditions in risk after discharge? Are specific clusters of chronic diseases particularly correlated with adverse health events? Moreover, how do common impairments and syndromes in older persons, such as cognitive impairment, functional impairment, difficulty with walking, sleep disturbance, and frailty, contribute to posthospitalization vulnerability? Would measurements of mobility and function immediately after discharge provide additional value in risk stratification beyond such measurements made during hospitalization?
HOSPITAL ENVIRONMENT
How does ambient sound, ambient light, shared rooms, and frequent awakening for vital signs checks, diagnostic tests, or medication administration affect sleep duration and quality, incident delirium, and in‐hospital complications? What influence do these factors have on postdischarge recovery of baseline sleep patterns and cognition? How does forced immobility from bed rest or restraints influence recovery of muscle mass and the function of arms and legs after discharge? How does fasting prior to diagnostic tests or therapeutic interventions impact recovery of weight, recovery of strength, and susceptibility to further illnesses after hospitalization?
CARE TRANSITIONS
What are the influences of organizational context on the success or failure of specific transitional care interventions? What is the relative importance of senior managerial commitment to improving postdischarge outcomes, the presence of local champions for quality, and an organization's culture of learning, collaboration, and belief in shared accountability? How does the particular way in which a program is implemented and managed with regard to its staffing, education of key personnel, available resources, methods for data collection, measurement of results, and approach to continuous quality improvement relate to its ability to reduce readmission?
SOCIAL AND ENVIRONMENTAL FACTORS
What particular types of emotional, informational, and instrumental supports are most critical after hospitalization to avoid subsequent adverse health events? How do financial issues contribute to difficulties with follow‐up care and medication management, adherence to dietary and activity recommendations, and levels of stress and anxiety following discharge? How does the home environment mitigate or exacerbate new vulnerabilities after hospitalization?
Ultimately, an improved understanding of the breadth of factors that predict recurrent medical illness after discharge, as signaled by readmission, and the manner in which they confer risk will improve both risk prediction and efforts to mitigate vulnerability after hospitalization. Ultimately, we need to learn how to align our hospital environments, transitional care interventions, and strategies for longitudinal engagement in ways that improve patients' recovery. The work by McAlister and colleagues[10] is a step in the right direction, as it breaks with the exclusive examination of traditional patient factors to incorporate complexities associated with discharge timing. Such investigations are necessary to truly understand the myriad sources of risk and recovery after hospital discharge.
ACKNOWLEDGMENTS
Disclosures: Dr. Dharmarajan is supported by grant K23AG048331‐01 from the National Institute on Aging and the American Federation for Aging Research through the Paul B. Beeson Career Development Award Program. Dr. Krumholz is supported by grant 1U01HL105270‐05 (Center for Cardiovascular Outcomes Research at Yale University) from the National Heart, Lung, and Blood Institute. The content is solely the responsibility of the authors and does not represent the official views of the National Institute on Aging; National Heart, Lung, and Blood Institute; or American Federation for Aging Research. Drs. Dharmarajan and Krumholz work under contract with the Centers for Medicare & Medicaid Services to develop and maintain performance measures. Dr. Krumholz is the chair of a cardiac scientific advisory board for UnitedHealth and is the recipient of research grants from Medtronic and from Johnson & Johnson, through Yale University, to develop methods of clinical trial data sharing.
- , , , et al. Use of hospital‐based acute care among patients recently discharged from the hospital. JAMA. 2013;309:364–371.
- , , . Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360:1418–1428.
- , , , . Hospitalization, restricted activity, and the development of disability among older persons. JAMA. 2004;292:2115–2124.
- , , , . Change in disability after hospitalization or restricted activity in older persons. JAMA. 2010;304:1919–1928.
- , , , et al. Trends in length of stay and short‐term outcomes among Medicare patients hospitalized for heart failure, 1993–2006. JAMA. 2010;303:2141–2147.
- , , , et al. Comparison of hospital risk‐standardized mortality rates calculated by using in‐hospital and 30‐day models: an observational study with implications for hospital profiling. Ann Intern Med. 2012;156:19–26.
- , , , et al. Diagnoses and timing of 30‐day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia. JAMA. 2013;309:355–363.
- . Post‐hospital syndrome—an acquired, transient condition of generalized risk. N Engl J Med. 2013;368:100–102.
- , . Hospital readmissions and the Affordable Care Act: paying for coordinated quality care. JAMA. 2011;306:1794–1795.
- , , , . Post‐discharge outcomes are similar for weekend versus weekday discharges for general internal medicine patients admitted to teaching hospitals. J Hosp Med. 2015;10(2):69–74.
- , , , , . Postdischarge outcomes in heart failure are better for teaching hospitals and weekday discharges. Circ Heart Fail. 2013;6:922–929.
- , . Risk of death or readmission among people discharged from hospital on Fridays. CMAJ. 2002;166:1672–1673.
- , , , et al. Predictors of short‐term rehospitalization following discharge of patients hospitalized with community‐acquired pneumonia. Chest. 2009;136:1079–1085.
- , , . Should hospitals keep their patients longer? The role of inpatient and outpatient care in reducing readmissions. NBER working paper no. 20499. Cambridge, MA: National Bureau of Economic Research; 2014.
- , , , et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306:1688–1698.
- , . Strategies to reduce 30‐day readmissions in older patients hospitalized with heart failure and acute myocardial infarction. Curr Geri Rep. 2014;3:306–315.
- , , . Postdischarge environment following heart failure hospitalization: expanding the view of hospital readmission. J Am Heart Assoc. 2013;2:e000116.
- , , , et al. Use of hospital‐based acute care among patients recently discharged from the hospital. JAMA. 2013;309:364–371.
- , , . Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360:1418–1428.
- , , , . Hospitalization, restricted activity, and the development of disability among older persons. JAMA. 2004;292:2115–2124.
- , , , . Change in disability after hospitalization or restricted activity in older persons. JAMA. 2010;304:1919–1928.
- , , , et al. Trends in length of stay and short‐term outcomes among Medicare patients hospitalized for heart failure, 1993–2006. JAMA. 2010;303:2141–2147.
- , , , et al. Comparison of hospital risk‐standardized mortality rates calculated by using in‐hospital and 30‐day models: an observational study with implications for hospital profiling. Ann Intern Med. 2012;156:19–26.
- , , , et al. Diagnoses and timing of 30‐day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia. JAMA. 2013;309:355–363.
- . Post‐hospital syndrome—an acquired, transient condition of generalized risk. N Engl J Med. 2013;368:100–102.
- , . Hospital readmissions and the Affordable Care Act: paying for coordinated quality care. JAMA. 2011;306:1794–1795.
- , , , . Post‐discharge outcomes are similar for weekend versus weekday discharges for general internal medicine patients admitted to teaching hospitals. J Hosp Med. 2015;10(2):69–74.
- , , , , . Postdischarge outcomes in heart failure are better for teaching hospitals and weekday discharges. Circ Heart Fail. 2013;6:922–929.
- , . Risk of death or readmission among people discharged from hospital on Fridays. CMAJ. 2002;166:1672–1673.
- , , , et al. Predictors of short‐term rehospitalization following discharge of patients hospitalized with community‐acquired pneumonia. Chest. 2009;136:1079–1085.
- , , . Should hospitals keep their patients longer? The role of inpatient and outpatient care in reducing readmissions. NBER working paper no. 20499. Cambridge, MA: National Bureau of Economic Research; 2014.
- , , , et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306:1688–1698.
- , . Strategies to reduce 30‐day readmissions in older patients hospitalized with heart failure and acute myocardial infarction. Curr Geri Rep. 2014;3:306–315.
- , , . Postdischarge environment following heart failure hospitalization: expanding the view of hospital readmission. J Am Heart Assoc. 2013;2:e000116.
Quantifying Treatment Intensity
Healthcare spending exceeded $2.5 trillion in 2007, and payments to hospitals represented the largest portion of this spending (more than 30%), equaling the combined cost of physician services and prescription drugs.[1, 2] Researchers and policymakers have emphasized the need to improve the value of hospital care in the United States, but this has been challenging, in part because of the difficulty in identifying hospitals that have high resource utilization relative to their peers.[3, 4, 5, 6, 7, 8, 9, 10, 11]
Most hospitals calculate their costs using internal accounting systems that determine resource utilization via relative value units (RVUs).[7, 8] RVU‐derived costs, also known as hospital reported costs, have proven to be an excellent method for quantifying what it costs a given hospital to provide a treatment, test, or procedure. However, RVU‐based costs are less useful for comparing resource utilization across hospitals because the cost to provide a treatment or service varies widely across hospitals. The cost of an item calculated using RVUs includes not just the item itself, but also a portion of the fixed costs of the hospital (overhead, labor, and infrastructure investments such as electronic records, new buildings, or expensive radiological or surgical equipment).[12] These costs vary by institution, patient population, region of the country, teaching status, and many other variables, making it difficult to identify resource utilization across hospitals.[13, 14]
Recently, a few claims‐based multi‐institutional datasets have begun incorporating item‐level RVU‐based costs derived directly from the cost accounting systems of participating institutions.[15] Such datasets allow researchers to compare reported costs of care from hospital to hospital, but because of the limitations we described above, they still cannot be used to answer the question: Which hospitals with higher costs of care are actually providing more treatments and services to patients?
To better facilitate the comparison of resource utilization patterns across hospitals, we standardized the unit costs of all treatments and services across hospitals by applying a single cost to every item across hospitals. This standardized cost allowed to compare utilization of that item (and the 15,000 other items in the database) across hospitals. We then compared estimates of resource utilization as measured by the 2 approaches: standardized and RVU‐based costs.
METHODS
Ethics Statement
All data were deidentified, by Premier, Inc., at both the hospital and patient level in accordance with the Health Insurance Portability and Accountability Act. The Yale University Human Investigation Committee reviewed the protocol for this study and determined that it is not considered to be human subjects research as defined by the Office of Human Research Protections.
Data Source
We conducted a cross‐sectional study using data from hospitals that participated in the database maintained by Premier Healthcare Informatics (Charlotte, NC) in the years 2009 to 2010. The Premier database is a voluntary, fee‐supported database created to measure quality and healthcare utilization.[3, 16, 17, 18] In 2010, it included detailed billing data from 500 hospitals in the United States, with more than 130 million cumulative hospital discharges. The detailed billing data includes all elements found in hospital claims derived from the uniform billing‐04 form, as well as an itemized, date‐stamped log of all items and services charged to the patient or insurer, such as medications, laboratory tests, and diagnostic and therapeutic services. The database includes approximately 15% of all US hospitalizations. Participating hospitals are similar to the composition of acute care hospitals nationwide. They represent all regions of the United States, and represent predominantly small‐ to mid‐sized nonteaching facilities that serve a largely urban population. The database also contains hospital reported costs at the item level as well as the total cost of the hospitalization. Approximately 75% of hospitals that participate submit RVU‐based costs taken from internal cost accounting systems. Because of our focus on comparing standardized costs to reported costs, we included only data from hospitals that use RVU‐based costs in this study.
Study Subjects
We included adult patients with a hospitalization recorded in the Premier database between January 1, 2009 and December 31, 2010, and a principal discharge diagnosis of heart failure (HF) (International Classification of Diseases, Ninth Revision, Clinical Modification codes: 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, 428.xx). We excluded transfers, patients assigned a pediatrician as the attending of record, and those who received a heart transplant or ventricular assist device during their stay. Because cost data are prone to extreme outliers, we excluded hospitalizations that were in the top 0.1% of length of stay, number of billing records, quantity of items billed, or total standardized cost. We also excluded hospitals that admitted fewer than 25 HF patients during the study period to reduce the possibility that a single high‐cost patient affected the hospital's cost profile.
Hospital Information
For each hospital included in the study, we recorded number of beds, teaching status, geographic region, and whether it served an urban or rural population.
Assignment of Standardized Costs
We defined reported cost as the RVU‐based cost per item in the database. We then calculated the median across hospitals for each item in the database and set this as the standardized unit cost of that item at every hospital (Figure 1). Once standardized costs were assigned at the item level, we summed the costs of all items assigned to each patient and calculated the standardized cost of a hospitalization per patient at each hospital.
Examination of Cost Variation
We compared the standardized and reported costs of hospitalizations using medians, interquartile ranges, and interquartile ratios (Q75/Q25). To examine whether standardized costs can reduce the noise due to differences in overhead and other fixed costs, we calculated, for each hospital, the coefficients of variation (CV) for per‐day reported and standardized costs and per‐hospitalization reported and standardized costs. We used the Fligner‐Killeen test to determine whether the variance of CVs was different for reported and standardized costs.[19]
Creation of Basket of Goods
Because there can be differences in the costs of items, the number and types of items administered during hospitalizations, 2 hospitals with similar reported costs for a hospitalization might deliver different quantities and combinations of treatments (Figure 1). We wished to demonstrate that there is variation in reported costs of items when the quantity and type of item is held constant, so we created a basket of items. We chose items that are commonly administered to patients with heart failure, but could have chosen any combination of items. The basket included a day of medical room and board, a day of intensive care unit (ICU) room and board, a single dose of ‐blocker, a single dose of angiotensin‐converting enzyme inhibitor, complete blood count, a B‐natriuretic peptide level, a chest radiograph, a chest computed tomography, and an echocardiogram. We then examined the range of hospitals' reported costs for this basket of goods using percentiles, medians, and interquartile ranges.
Reported to Standardized Cost Ratio
Next, we calculated standardized costs of hospitalizations for included hospitals and examined the relationship between hospitals' mean reported costs and mean standardized costs. This ratio could help diagnose the mechanism of high reported costs for a hospital, because high reported costs with low utilization would indicate high fixed costs, while high reported costs with high utilization would indicate greater use of tests and treatments. We assigned hospitals to strata based on reported costs greater than standardized costs by more than 25%, reported costs within 25% of standardized costs, and reported costs less than standardized costs by more than 25%. We examined the association between hospital characteristics and strata using a 2 test. All analyses were carried out using SAS version 9.3 (SAS Institute Inc., Cary, NC).
RESULTS
The 234 hospitals included in the analysis contributed a total of 165,647 hospitalizations, with the number of hospitalizations ranging from 33 to 2,772 hospitalizations per hospital (see Supporting Table 1 in the online version of this article). Most were located in urban areas (84%), and many were in the southern United States (42%). The median hospital reported cost per hospitalization was $6,535, with an interquartile range of $5,541 to $7,454. The median standardized cost per hospitalization was $6,602, with a range of $5,866 to $7,386. The interquartile ratio (Q75/Q25) of the reported costs of a hospitalization was 1.35. After costs were standardized, the interquartile ratio fell to 1.26, indicating that variation decreased. We found that the median hospital reported cost per day was $1,651, with an IQR of $1,400 to $1,933 (ratio 1.38), whereas the median standardized cost per day was $1,640, with an IQR of $1,511 to $1,812 (ratio 1.20).
There were more than 15,000 items (eg, treatments, tests, and supplies) that received a standardized charge code in our cohort. These were divided into 11 summary departments and 40 standard departments (see Supporting Table 2 in the online version of this article). We observed a high level of variation in the reported costs of individual items: the reported costs of a day of room and board in an ICU ranged from $773 at hospitals at the 10th percentile to $2,471 at the 90th percentile (Table 1.). The standardized cost of a day of ICU room and board was $1,577. We also observed variation in the reported costs of items across item categories. Although a day of medical room and board showed a 3‐fold difference between the 10th and 90th percentile, we observed a more than 10‐fold difference in the reported cost of an echocardiogram, from $31 at the 10th percentile to $356 at the 90th percentile. After examining the hospital‐level cost for a basket of goods, we found variation in the reported costs for these items across hospitals, with a 10th percentile cost of $1,552 and a 90th percentile cost of $3,967.
| Reported Costs | 10th Percentile | 25th Percentile | 75th Percentile | 90th Percentile | Median (Standardized Cost) |
|---|---|---|---|---|---|
| |||||
| Item | |||||
| Day of medical | 490.03 | 586.41 | 889.95 | 1121.20 | 722.59 |
| Day of ICU | 773.01 | 1275.84 | 1994.81 | 2471.75 | 1577.93 |
| Complete blood count | 6.87 | 9.34 | 18.34 | 23.46 | 13.07 |
| B‐natriuretic peptide | 12.13 | 19.22 | 44.19 | 60.56 | 28.23 |
| Metoprolol | 0.20 | 0.68 | 2.67 | 3.74 | 1.66 |
| Lisinopril | 0.28 | 1.02 | 2.79 | 4.06 | 1.72 |
| Spironolactone | 0.22 | 0.53 | 2.68 | 3.83 | 1.63 |
| Furosemide | 1.27 | 2.45 | 5.73 | 8.12 | 3.82 |
| Chest x‐ray | 43.88 | 51.54 | 89.96 | 117.16 | 67.45 |
| Echocardiogram | 31.53 | 98.63 | 244.63 | 356.50 | 159.07 |
| Chest CT (w & w/o contrast) | 65.17 | 83.99 | 157.23 | 239.27 | 110.76 |
| Noninvasive positive pressure ventilation | 126.23 | 127.25 | 370.44 | 514.67 | 177.24 |
| Electrocardiogram | 12.08 | 18.77 | 42.74 | 64.94 | 29.78 |
| Total basket | 1552.50 | 2157.85 | 3417.34 | 3967.78 | 2710.49 |
We found that 46 (20%) hospitals had reported costs of hospitalizations that were 25% greater than standardized costs (Figure 2). This group of hospitals had overestimated reported costs of utilization; 146 (62%) had reported costs within 25% of standardized costs, and 42 (17%) had reported costs that were 25% less than standardized costs (indicating that reported costs underestimated utilization). We examined the relationship between hospital characteristics and strata and found no significant association between the reported to standardized cost ratio and number of beds, teaching status, or urban location (Table 2). Hospitals in the Midwest and South were more likely to have a lower reported cost of hospitalizations, whereas hospitals in the West were more likely to have higher reported costs (P<0.001). When using the CV to compare reported costs to standardized costs, we found that per‐day standardized costs showed reduced variance (P=0.0238), but there was no significant difference in variance of the reported and standardized costs when examining the entire hospitalization (P=0.1423). At the level of the hospitalization, the Spearman correlation coefficient between reported and standardized cost was 0.89.
| Reported Greater Than Standardized by >25%, n (%) | Reported Within 25% (2‐tailed) of Standardized, n (%) | Reported Less Than Standardized by >25%, n (%) | P for 2 Test | |
|---|---|---|---|---|
| Total | 46 (19.7) | 146 (62.4) | 42 (17.0) | |
| No. of beds | 0.2313 | |||
| <200 | 19 (41.3) | 40 (27.4) | 12 (28.6) | |
| 200400 | 14 (30.4) | 67 (45.9) | 15 (35.7) | |
| >400 | 13 (28.3) | 39 (26.7) | 15 (35.7) | |
| Teaching | 0.8278 | |||
| Yes | 13 (28.3) | 45 (30.8) | 11 (26.2) | |
| No | 33 (71.7) | 101 (69.2) | 31 (73.8) | |
| Region | <0.0001 | |||
| Midwest | 7 (15.2) | 43 (29.5) | 19 (45.2) | |
| Northeast | 6 (13.0) | 18 (12.3) | 3 (7.1) | |
| South | 14 (30.4) | 64 (43.8) | 20 (47.6) | |
| West | 19 (41.3) | 21 (14.4) | 0 (0) | |
| Urban vs rural | 36 (78.3) | 128 (87.7) | 33 (78.6) | 0.1703 |
To better understand how hospitals can achieve high reported costs through different mechanisms, we more closely examined 3 hospitals with similar reported costs (Figure 3). These hospitals represented low, average, and high utilization according to their standardized costs, but had similar average per‐hospitalization reported costs: $11,643, $11,787, and $11,892, respectively. The corresponding standardized costs were $8,757, $11,169, and $15,978. The hospital with high utilization ($15,978 in standardized costs) was accounted for by increased use of supplies and other services. In contrast, the low‐ and average‐utilization hospitals had proportionally lower standardized costs across categories, with the greatest percentage of spending going toward room and board (includes nursing).
DISCUSSION
In a large national sample of hospitals, we observed variation in the reported costs for a uniform basket of goods, with a more than 2‐fold difference in cost between the 10th and 90th percentile hospitals. These findings suggest that reported costs have limited ability to reliably describe differences in utilization across hospitals. In contrast, when we applied standardized costs, the variance of per‐day costs decreased significantly, and the interquartile ratio of per‐day and hospitalization costs decreased as well, suggesting less variation in utilization across hospitals than would have been inferred from a comparison of reported costs. Applying a single, standard cost to all items can facilitate comparisons of utilization between hospitals (Figure 1). Standardized costs will give hospitals the potential to compare their utilization to their competitors and will facilitate research that examines the comparative effectiveness of high and low utilization in the management of medical and surgical conditions.
The reported to standardized cost ratio is another useful tool. It indicates whether the hospital's reported costs exaggerate its utilization relative to other hospitals. In this study, we found that a significant proportion of hospitals (20%) had reported costs that exceeded standardized costs by more than 25%. These hospitals have higher infrastructure, labor, or acquisition costs relative to their peers. To the extent that these hospitals might wish to lower the cost of care at their institution, they could focus on renegotiating purchasing or labor contracts, identifying areas where they may be overstaffed, or holding off on future infrastructure investments (Table 3).[14] In contrast, 17% of hospitals had reported costs that were 25% less than standardized costs. High‐cost hospitals in this group are therefore providing more treatments and testing to patients relative to their peers and could focus cost‐control efforts on reducing unnecessary utilization and duplicative testing.[20] Our examination of the hospital with high reported costs and very high utilization revealed a high percentage of supplies and other items, which is a category used primarily for nursing expenditures (Figure 3). Because the use of nursing services is directly related to days spent in the hospital, this hospital may wish to more closely examine specific strategies for reducing length of stay.
| High Reported Costs/High Standardized Costs | High Reported Costs/Low Standardized Costs | Low Reported Costs/High Standardized Costs | Low Reported Costs/Low Standardized Costs | |
|---|---|---|---|---|
| Utilization | High | Low | High | Low |
| Severity of illness | Likely to be higher | Likely to be lower | Likely to be higher | Likely to be lower |
| Practice style | Likely to be more intense | Likely to be less intense | Likely to be more intense | Likely to be less intense |
| Fixed costs | High or average | High | Low | Low |
| Infrastructure costs | Likely to be higher | Likely to be higher | Likely to be lower | Likely to be lower |
| Labor costs | Likely to be higher | Likely to be higher | Likely to be lower | Likely to be lower |
| Reported‐to‐standardized cost ratio | Close to 1 | >1 | <1 | Close to 1 |
| Causes of high costs | High utilization, high fixed costs, or both | High acquisition costs, high labor costs, or expensive infrastructure | High utilization | |
| Interventions to reduce costs | Work with clinicians to alter practice style, consider renegotiating cost of acquisitions, hold off on new infrastructure investments | Consider renegotiating cost of acquisitions, hold off on new infrastructure investments, consider reducing size of labor force | Work with clinicians to alter practice style | |
| Usefulness of reported‐ to‐standardized cost ratio | Less useful | More useful | More useful | Less useful |
We did not find a consistent association between the reported to standardized cost ratio and hospital characteristics. This is an important finding that contradicts prior work examining associations between hospital characteristics and costs for heart failure patients,[21] further indicating the complexity of the relationship between fixed costs and variable costs and the difficulty in adjusting reported costs to calculate utilization. For example, small hospitals may have higher acquisition costs and more supply chain difficulties, but they may also have less technology, lower overhead costs, and fewer specialists to order tests and procedures. Hospital characteristics, such as urban location and teaching status, are commonly used as adjustors in cost studies because hospitals in urban areas with teaching missions (which often provide care to low‐income populations) are assumed to have higher fixed costs,[3, 4, 5, 6] but the lack of a consistent relationship between these characteristics and the standardized cost ratio may indicate that using these factors as adjustors for cost may not be effective and could even obscure differences in utilization between hospitals. Notably, we did find an association between hospital region and the reported to standardized cost ratio, but we hesitate to draw conclusions from this finding because the Premier database is imbalanced in terms of regional representation, with fewer hospitals in the Midwest and West and the bulk of the hospitals in the South.
Although standardized costs have great potential, this method has limitations as well. Standardized costs can only be applied when detailed billing data with item‐level costs are available. This is because calculation of standardized costs requires taking the median of item costs and applying the median cost across the database, maintaining the integrity of the relative cost of items to one another. The relative cost of items is preserved (ie, magnetic resonance imaging still costs more than an aspirin), which maintains the general scheme of RVU‐based costs while removing the noise of varying RVU‐based costs across hospitals.[7] Application of an arbitrary item cost would result in the loss of this relative cost difference. Because item costs are not available in traditional administrative datasets, these datasets would not be amenable to this method. However, highly detailed billing data are now being shared by hundreds of hospitals in the Premier network and the University Health System Consortium. These data are widely available to investigators, meaning that the generalizability of this method will only improve over time. It was also a limitation of the study that we chose a limited basket of items common to patients with heart failure to describe the range of reported costs and to provide a standardized snapshot by which to compare hospitals. Because we only included a few items, we may have overestimated or underestimated the range of reported costs for such a basket.
Standardized costs are a novel method for comparing utilization across hospitals. Used properly, they will help identify high‐ and low‐intensity providers of hospital care.
- Health care costs–a primer. Kaiser Family Foundation Web site. Available at: http://www.kff.org/insurance/7670.cfm. Accessed July 20, 2012.
- . Explaining high health care spending in the United States: an international comparison of supply, utilization, prices, and quality. The Commonwealth Fund. 2012. Available at: http://www.commonwealthfund.org/Publications/Issue‐Briefs/2012/May/High‐Health‐Care‐Spending. aspx. Accessed on July 20, 2012.
- , , , , , . The relationship between hospital spending and mortality in patients with sepsis. Arch Intern Med. 2011;171(4):292–299.
- , , , . The elusive connection between health care spending and quality. Health Aff (Millwood). 2009;28(1):w119–w123.
- , , , . Hospital quality and intensity of spending: is there an association? Health Aff (Millwood). 2009;28(4):w566–w572.
- , , , , . Measuring efficiency: the association of hospital costs and quality of care. Health Aff (Millwood). 2009;28(3):897–906.
- , . Assigning resources to health care use for health services research: options and consequences. Med Care. 2009;47(7 suppl 1):S70–S75.
- , , , , . Health care costing: data, methods, current applications. Med Care. 2009;47(7 suppl 1):S1–S6.
- . Determination of VA health care costs. Med Care Res Rev. 2003;60(3 suppl):124S–141S.
- . An improved set of standards for finding cost for cost‐effectiveness analysis. Med Care. 2009;47(7 suppl 1):S82–S88.
- , , , et al. Comparison of approaches for estimating prevalence costs of care for cancer patients: what is the impact of data source? Med Care. 2009;47(7 suppl 1):S64–S69.
- . Principles involved in costing. Med J Aust. 1990;153Suppl:S10–S12.
- . Spending more through “cost control:” our obsessive quest to gut the hospital. Health Aff (Millwood). 1996;15(2):145–154.
- , , , et al. Distribution of variable vs. fixed costs of hospital care. JAMA. 1999;281(7):644–649.
- . Administrative and claims records as sources of health care cost data. Med Care. 2009;47(7 suppl 1):S51–S55.
- , , , , , . Perioperative beta‐blocker therapy and mortality after major noncardiac surgery. N Engl J Med. 2005;353(4):349–361.
- , , , et al. Public reporting and pay for performance in hospital quality improvement. N Engl J Med. 2007;356(5):486–496.
- , , , et al. Procedure intensity and the cost of care. Circ Cardiovasc Qual Outcomes. 2012;5(3):308–313.
- , , . A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data. Technometrics. 1981;23:351–361.
- , , . Beyond the efficiency index: finding a better way to reduce overuse and increase efficiency in physician care. Health Aff (Millwood). 2008;27(4):w250–w259.
- , , . The association between hospital volume and processes, outcomes, and costs of care for congestive heart failure. Ann Intern Med. 2011;154(2):94–102.
Healthcare spending exceeded $2.5 trillion in 2007, and payments to hospitals represented the largest portion of this spending (more than 30%), equaling the combined cost of physician services and prescription drugs.[1, 2] Researchers and policymakers have emphasized the need to improve the value of hospital care in the United States, but this has been challenging, in part because of the difficulty in identifying hospitals that have high resource utilization relative to their peers.[3, 4, 5, 6, 7, 8, 9, 10, 11]
Most hospitals calculate their costs using internal accounting systems that determine resource utilization via relative value units (RVUs).[7, 8] RVU‐derived costs, also known as hospital reported costs, have proven to be an excellent method for quantifying what it costs a given hospital to provide a treatment, test, or procedure. However, RVU‐based costs are less useful for comparing resource utilization across hospitals because the cost to provide a treatment or service varies widely across hospitals. The cost of an item calculated using RVUs includes not just the item itself, but also a portion of the fixed costs of the hospital (overhead, labor, and infrastructure investments such as electronic records, new buildings, or expensive radiological or surgical equipment).[12] These costs vary by institution, patient population, region of the country, teaching status, and many other variables, making it difficult to identify resource utilization across hospitals.[13, 14]
Recently, a few claims‐based multi‐institutional datasets have begun incorporating item‐level RVU‐based costs derived directly from the cost accounting systems of participating institutions.[15] Such datasets allow researchers to compare reported costs of care from hospital to hospital, but because of the limitations we described above, they still cannot be used to answer the question: Which hospitals with higher costs of care are actually providing more treatments and services to patients?
To better facilitate the comparison of resource utilization patterns across hospitals, we standardized the unit costs of all treatments and services across hospitals by applying a single cost to every item across hospitals. This standardized cost allowed to compare utilization of that item (and the 15,000 other items in the database) across hospitals. We then compared estimates of resource utilization as measured by the 2 approaches: standardized and RVU‐based costs.
METHODS
Ethics Statement
All data were deidentified, by Premier, Inc., at both the hospital and patient level in accordance with the Health Insurance Portability and Accountability Act. The Yale University Human Investigation Committee reviewed the protocol for this study and determined that it is not considered to be human subjects research as defined by the Office of Human Research Protections.
Data Source
We conducted a cross‐sectional study using data from hospitals that participated in the database maintained by Premier Healthcare Informatics (Charlotte, NC) in the years 2009 to 2010. The Premier database is a voluntary, fee‐supported database created to measure quality and healthcare utilization.[3, 16, 17, 18] In 2010, it included detailed billing data from 500 hospitals in the United States, with more than 130 million cumulative hospital discharges. The detailed billing data includes all elements found in hospital claims derived from the uniform billing‐04 form, as well as an itemized, date‐stamped log of all items and services charged to the patient or insurer, such as medications, laboratory tests, and diagnostic and therapeutic services. The database includes approximately 15% of all US hospitalizations. Participating hospitals are similar to the composition of acute care hospitals nationwide. They represent all regions of the United States, and represent predominantly small‐ to mid‐sized nonteaching facilities that serve a largely urban population. The database also contains hospital reported costs at the item level as well as the total cost of the hospitalization. Approximately 75% of hospitals that participate submit RVU‐based costs taken from internal cost accounting systems. Because of our focus on comparing standardized costs to reported costs, we included only data from hospitals that use RVU‐based costs in this study.
Study Subjects
We included adult patients with a hospitalization recorded in the Premier database between January 1, 2009 and December 31, 2010, and a principal discharge diagnosis of heart failure (HF) (International Classification of Diseases, Ninth Revision, Clinical Modification codes: 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, 428.xx). We excluded transfers, patients assigned a pediatrician as the attending of record, and those who received a heart transplant or ventricular assist device during their stay. Because cost data are prone to extreme outliers, we excluded hospitalizations that were in the top 0.1% of length of stay, number of billing records, quantity of items billed, or total standardized cost. We also excluded hospitals that admitted fewer than 25 HF patients during the study period to reduce the possibility that a single high‐cost patient affected the hospital's cost profile.
Hospital Information
For each hospital included in the study, we recorded number of beds, teaching status, geographic region, and whether it served an urban or rural population.
Assignment of Standardized Costs
We defined reported cost as the RVU‐based cost per item in the database. We then calculated the median across hospitals for each item in the database and set this as the standardized unit cost of that item at every hospital (Figure 1). Once standardized costs were assigned at the item level, we summed the costs of all items assigned to each patient and calculated the standardized cost of a hospitalization per patient at each hospital.
Examination of Cost Variation
We compared the standardized and reported costs of hospitalizations using medians, interquartile ranges, and interquartile ratios (Q75/Q25). To examine whether standardized costs can reduce the noise due to differences in overhead and other fixed costs, we calculated, for each hospital, the coefficients of variation (CV) for per‐day reported and standardized costs and per‐hospitalization reported and standardized costs. We used the Fligner‐Killeen test to determine whether the variance of CVs was different for reported and standardized costs.[19]
Creation of Basket of Goods
Because there can be differences in the costs of items, the number and types of items administered during hospitalizations, 2 hospitals with similar reported costs for a hospitalization might deliver different quantities and combinations of treatments (Figure 1). We wished to demonstrate that there is variation in reported costs of items when the quantity and type of item is held constant, so we created a basket of items. We chose items that are commonly administered to patients with heart failure, but could have chosen any combination of items. The basket included a day of medical room and board, a day of intensive care unit (ICU) room and board, a single dose of ‐blocker, a single dose of angiotensin‐converting enzyme inhibitor, complete blood count, a B‐natriuretic peptide level, a chest radiograph, a chest computed tomography, and an echocardiogram. We then examined the range of hospitals' reported costs for this basket of goods using percentiles, medians, and interquartile ranges.
Reported to Standardized Cost Ratio
Next, we calculated standardized costs of hospitalizations for included hospitals and examined the relationship between hospitals' mean reported costs and mean standardized costs. This ratio could help diagnose the mechanism of high reported costs for a hospital, because high reported costs with low utilization would indicate high fixed costs, while high reported costs with high utilization would indicate greater use of tests and treatments. We assigned hospitals to strata based on reported costs greater than standardized costs by more than 25%, reported costs within 25% of standardized costs, and reported costs less than standardized costs by more than 25%. We examined the association between hospital characteristics and strata using a 2 test. All analyses were carried out using SAS version 9.3 (SAS Institute Inc., Cary, NC).
RESULTS
The 234 hospitals included in the analysis contributed a total of 165,647 hospitalizations, with the number of hospitalizations ranging from 33 to 2,772 hospitalizations per hospital (see Supporting Table 1 in the online version of this article). Most were located in urban areas (84%), and many were in the southern United States (42%). The median hospital reported cost per hospitalization was $6,535, with an interquartile range of $5,541 to $7,454. The median standardized cost per hospitalization was $6,602, with a range of $5,866 to $7,386. The interquartile ratio (Q75/Q25) of the reported costs of a hospitalization was 1.35. After costs were standardized, the interquartile ratio fell to 1.26, indicating that variation decreased. We found that the median hospital reported cost per day was $1,651, with an IQR of $1,400 to $1,933 (ratio 1.38), whereas the median standardized cost per day was $1,640, with an IQR of $1,511 to $1,812 (ratio 1.20).
There were more than 15,000 items (eg, treatments, tests, and supplies) that received a standardized charge code in our cohort. These were divided into 11 summary departments and 40 standard departments (see Supporting Table 2 in the online version of this article). We observed a high level of variation in the reported costs of individual items: the reported costs of a day of room and board in an ICU ranged from $773 at hospitals at the 10th percentile to $2,471 at the 90th percentile (Table 1.). The standardized cost of a day of ICU room and board was $1,577. We also observed variation in the reported costs of items across item categories. Although a day of medical room and board showed a 3‐fold difference between the 10th and 90th percentile, we observed a more than 10‐fold difference in the reported cost of an echocardiogram, from $31 at the 10th percentile to $356 at the 90th percentile. After examining the hospital‐level cost for a basket of goods, we found variation in the reported costs for these items across hospitals, with a 10th percentile cost of $1,552 and a 90th percentile cost of $3,967.
| Reported Costs | 10th Percentile | 25th Percentile | 75th Percentile | 90th Percentile | Median (Standardized Cost) |
|---|---|---|---|---|---|
| |||||
| Item | |||||
| Day of medical | 490.03 | 586.41 | 889.95 | 1121.20 | 722.59 |
| Day of ICU | 773.01 | 1275.84 | 1994.81 | 2471.75 | 1577.93 |
| Complete blood count | 6.87 | 9.34 | 18.34 | 23.46 | 13.07 |
| B‐natriuretic peptide | 12.13 | 19.22 | 44.19 | 60.56 | 28.23 |
| Metoprolol | 0.20 | 0.68 | 2.67 | 3.74 | 1.66 |
| Lisinopril | 0.28 | 1.02 | 2.79 | 4.06 | 1.72 |
| Spironolactone | 0.22 | 0.53 | 2.68 | 3.83 | 1.63 |
| Furosemide | 1.27 | 2.45 | 5.73 | 8.12 | 3.82 |
| Chest x‐ray | 43.88 | 51.54 | 89.96 | 117.16 | 67.45 |
| Echocardiogram | 31.53 | 98.63 | 244.63 | 356.50 | 159.07 |
| Chest CT (w & w/o contrast) | 65.17 | 83.99 | 157.23 | 239.27 | 110.76 |
| Noninvasive positive pressure ventilation | 126.23 | 127.25 | 370.44 | 514.67 | 177.24 |
| Electrocardiogram | 12.08 | 18.77 | 42.74 | 64.94 | 29.78 |
| Total basket | 1552.50 | 2157.85 | 3417.34 | 3967.78 | 2710.49 |
We found that 46 (20%) hospitals had reported costs of hospitalizations that were 25% greater than standardized costs (Figure 2). This group of hospitals had overestimated reported costs of utilization; 146 (62%) had reported costs within 25% of standardized costs, and 42 (17%) had reported costs that were 25% less than standardized costs (indicating that reported costs underestimated utilization). We examined the relationship between hospital characteristics and strata and found no significant association between the reported to standardized cost ratio and number of beds, teaching status, or urban location (Table 2). Hospitals in the Midwest and South were more likely to have a lower reported cost of hospitalizations, whereas hospitals in the West were more likely to have higher reported costs (P<0.001). When using the CV to compare reported costs to standardized costs, we found that per‐day standardized costs showed reduced variance (P=0.0238), but there was no significant difference in variance of the reported and standardized costs when examining the entire hospitalization (P=0.1423). At the level of the hospitalization, the Spearman correlation coefficient between reported and standardized cost was 0.89.
| Reported Greater Than Standardized by >25%, n (%) | Reported Within 25% (2‐tailed) of Standardized, n (%) | Reported Less Than Standardized by >25%, n (%) | P for 2 Test | |
|---|---|---|---|---|
| Total | 46 (19.7) | 146 (62.4) | 42 (17.0) | |
| No. of beds | 0.2313 | |||
| <200 | 19 (41.3) | 40 (27.4) | 12 (28.6) | |
| 200400 | 14 (30.4) | 67 (45.9) | 15 (35.7) | |
| >400 | 13 (28.3) | 39 (26.7) | 15 (35.7) | |
| Teaching | 0.8278 | |||
| Yes | 13 (28.3) | 45 (30.8) | 11 (26.2) | |
| No | 33 (71.7) | 101 (69.2) | 31 (73.8) | |
| Region | <0.0001 | |||
| Midwest | 7 (15.2) | 43 (29.5) | 19 (45.2) | |
| Northeast | 6 (13.0) | 18 (12.3) | 3 (7.1) | |
| South | 14 (30.4) | 64 (43.8) | 20 (47.6) | |
| West | 19 (41.3) | 21 (14.4) | 0 (0) | |
| Urban vs rural | 36 (78.3) | 128 (87.7) | 33 (78.6) | 0.1703 |
To better understand how hospitals can achieve high reported costs through different mechanisms, we more closely examined 3 hospitals with similar reported costs (Figure 3). These hospitals represented low, average, and high utilization according to their standardized costs, but had similar average per‐hospitalization reported costs: $11,643, $11,787, and $11,892, respectively. The corresponding standardized costs were $8,757, $11,169, and $15,978. The hospital with high utilization ($15,978 in standardized costs) was accounted for by increased use of supplies and other services. In contrast, the low‐ and average‐utilization hospitals had proportionally lower standardized costs across categories, with the greatest percentage of spending going toward room and board (includes nursing).
DISCUSSION
In a large national sample of hospitals, we observed variation in the reported costs for a uniform basket of goods, with a more than 2‐fold difference in cost between the 10th and 90th percentile hospitals. These findings suggest that reported costs have limited ability to reliably describe differences in utilization across hospitals. In contrast, when we applied standardized costs, the variance of per‐day costs decreased significantly, and the interquartile ratio of per‐day and hospitalization costs decreased as well, suggesting less variation in utilization across hospitals than would have been inferred from a comparison of reported costs. Applying a single, standard cost to all items can facilitate comparisons of utilization between hospitals (Figure 1). Standardized costs will give hospitals the potential to compare their utilization to their competitors and will facilitate research that examines the comparative effectiveness of high and low utilization in the management of medical and surgical conditions.
The reported to standardized cost ratio is another useful tool. It indicates whether the hospital's reported costs exaggerate its utilization relative to other hospitals. In this study, we found that a significant proportion of hospitals (20%) had reported costs that exceeded standardized costs by more than 25%. These hospitals have higher infrastructure, labor, or acquisition costs relative to their peers. To the extent that these hospitals might wish to lower the cost of care at their institution, they could focus on renegotiating purchasing or labor contracts, identifying areas where they may be overstaffed, or holding off on future infrastructure investments (Table 3).[14] In contrast, 17% of hospitals had reported costs that were 25% less than standardized costs. High‐cost hospitals in this group are therefore providing more treatments and testing to patients relative to their peers and could focus cost‐control efforts on reducing unnecessary utilization and duplicative testing.[20] Our examination of the hospital with high reported costs and very high utilization revealed a high percentage of supplies and other items, which is a category used primarily for nursing expenditures (Figure 3). Because the use of nursing services is directly related to days spent in the hospital, this hospital may wish to more closely examine specific strategies for reducing length of stay.
| High Reported Costs/High Standardized Costs | High Reported Costs/Low Standardized Costs | Low Reported Costs/High Standardized Costs | Low Reported Costs/Low Standardized Costs | |
|---|---|---|---|---|
| Utilization | High | Low | High | Low |
| Severity of illness | Likely to be higher | Likely to be lower | Likely to be higher | Likely to be lower |
| Practice style | Likely to be more intense | Likely to be less intense | Likely to be more intense | Likely to be less intense |
| Fixed costs | High or average | High | Low | Low |
| Infrastructure costs | Likely to be higher | Likely to be higher | Likely to be lower | Likely to be lower |
| Labor costs | Likely to be higher | Likely to be higher | Likely to be lower | Likely to be lower |
| Reported‐to‐standardized cost ratio | Close to 1 | >1 | <1 | Close to 1 |
| Causes of high costs | High utilization, high fixed costs, or both | High acquisition costs, high labor costs, or expensive infrastructure | High utilization | |
| Interventions to reduce costs | Work with clinicians to alter practice style, consider renegotiating cost of acquisitions, hold off on new infrastructure investments | Consider renegotiating cost of acquisitions, hold off on new infrastructure investments, consider reducing size of labor force | Work with clinicians to alter practice style | |
| Usefulness of reported‐ to‐standardized cost ratio | Less useful | More useful | More useful | Less useful |
We did not find a consistent association between the reported to standardized cost ratio and hospital characteristics. This is an important finding that contradicts prior work examining associations between hospital characteristics and costs for heart failure patients,[21] further indicating the complexity of the relationship between fixed costs and variable costs and the difficulty in adjusting reported costs to calculate utilization. For example, small hospitals may have higher acquisition costs and more supply chain difficulties, but they may also have less technology, lower overhead costs, and fewer specialists to order tests and procedures. Hospital characteristics, such as urban location and teaching status, are commonly used as adjustors in cost studies because hospitals in urban areas with teaching missions (which often provide care to low‐income populations) are assumed to have higher fixed costs,[3, 4, 5, 6] but the lack of a consistent relationship between these characteristics and the standardized cost ratio may indicate that using these factors as adjustors for cost may not be effective and could even obscure differences in utilization between hospitals. Notably, we did find an association between hospital region and the reported to standardized cost ratio, but we hesitate to draw conclusions from this finding because the Premier database is imbalanced in terms of regional representation, with fewer hospitals in the Midwest and West and the bulk of the hospitals in the South.
Although standardized costs have great potential, this method has limitations as well. Standardized costs can only be applied when detailed billing data with item‐level costs are available. This is because calculation of standardized costs requires taking the median of item costs and applying the median cost across the database, maintaining the integrity of the relative cost of items to one another. The relative cost of items is preserved (ie, magnetic resonance imaging still costs more than an aspirin), which maintains the general scheme of RVU‐based costs while removing the noise of varying RVU‐based costs across hospitals.[7] Application of an arbitrary item cost would result in the loss of this relative cost difference. Because item costs are not available in traditional administrative datasets, these datasets would not be amenable to this method. However, highly detailed billing data are now being shared by hundreds of hospitals in the Premier network and the University Health System Consortium. These data are widely available to investigators, meaning that the generalizability of this method will only improve over time. It was also a limitation of the study that we chose a limited basket of items common to patients with heart failure to describe the range of reported costs and to provide a standardized snapshot by which to compare hospitals. Because we only included a few items, we may have overestimated or underestimated the range of reported costs for such a basket.
Standardized costs are a novel method for comparing utilization across hospitals. Used properly, they will help identify high‐ and low‐intensity providers of hospital care.
Healthcare spending exceeded $2.5 trillion in 2007, and payments to hospitals represented the largest portion of this spending (more than 30%), equaling the combined cost of physician services and prescription drugs.[1, 2] Researchers and policymakers have emphasized the need to improve the value of hospital care in the United States, but this has been challenging, in part because of the difficulty in identifying hospitals that have high resource utilization relative to their peers.[3, 4, 5, 6, 7, 8, 9, 10, 11]
Most hospitals calculate their costs using internal accounting systems that determine resource utilization via relative value units (RVUs).[7, 8] RVU‐derived costs, also known as hospital reported costs, have proven to be an excellent method for quantifying what it costs a given hospital to provide a treatment, test, or procedure. However, RVU‐based costs are less useful for comparing resource utilization across hospitals because the cost to provide a treatment or service varies widely across hospitals. The cost of an item calculated using RVUs includes not just the item itself, but also a portion of the fixed costs of the hospital (overhead, labor, and infrastructure investments such as electronic records, new buildings, or expensive radiological or surgical equipment).[12] These costs vary by institution, patient population, region of the country, teaching status, and many other variables, making it difficult to identify resource utilization across hospitals.[13, 14]
Recently, a few claims‐based multi‐institutional datasets have begun incorporating item‐level RVU‐based costs derived directly from the cost accounting systems of participating institutions.[15] Such datasets allow researchers to compare reported costs of care from hospital to hospital, but because of the limitations we described above, they still cannot be used to answer the question: Which hospitals with higher costs of care are actually providing more treatments and services to patients?
To better facilitate the comparison of resource utilization patterns across hospitals, we standardized the unit costs of all treatments and services across hospitals by applying a single cost to every item across hospitals. This standardized cost allowed to compare utilization of that item (and the 15,000 other items in the database) across hospitals. We then compared estimates of resource utilization as measured by the 2 approaches: standardized and RVU‐based costs.
METHODS
Ethics Statement
All data were deidentified, by Premier, Inc., at both the hospital and patient level in accordance with the Health Insurance Portability and Accountability Act. The Yale University Human Investigation Committee reviewed the protocol for this study and determined that it is not considered to be human subjects research as defined by the Office of Human Research Protections.
Data Source
We conducted a cross‐sectional study using data from hospitals that participated in the database maintained by Premier Healthcare Informatics (Charlotte, NC) in the years 2009 to 2010. The Premier database is a voluntary, fee‐supported database created to measure quality and healthcare utilization.[3, 16, 17, 18] In 2010, it included detailed billing data from 500 hospitals in the United States, with more than 130 million cumulative hospital discharges. The detailed billing data includes all elements found in hospital claims derived from the uniform billing‐04 form, as well as an itemized, date‐stamped log of all items and services charged to the patient or insurer, such as medications, laboratory tests, and diagnostic and therapeutic services. The database includes approximately 15% of all US hospitalizations. Participating hospitals are similar to the composition of acute care hospitals nationwide. They represent all regions of the United States, and represent predominantly small‐ to mid‐sized nonteaching facilities that serve a largely urban population. The database also contains hospital reported costs at the item level as well as the total cost of the hospitalization. Approximately 75% of hospitals that participate submit RVU‐based costs taken from internal cost accounting systems. Because of our focus on comparing standardized costs to reported costs, we included only data from hospitals that use RVU‐based costs in this study.
Study Subjects
We included adult patients with a hospitalization recorded in the Premier database between January 1, 2009 and December 31, 2010, and a principal discharge diagnosis of heart failure (HF) (International Classification of Diseases, Ninth Revision, Clinical Modification codes: 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, 428.xx). We excluded transfers, patients assigned a pediatrician as the attending of record, and those who received a heart transplant or ventricular assist device during their stay. Because cost data are prone to extreme outliers, we excluded hospitalizations that were in the top 0.1% of length of stay, number of billing records, quantity of items billed, or total standardized cost. We also excluded hospitals that admitted fewer than 25 HF patients during the study period to reduce the possibility that a single high‐cost patient affected the hospital's cost profile.
Hospital Information
For each hospital included in the study, we recorded number of beds, teaching status, geographic region, and whether it served an urban or rural population.
Assignment of Standardized Costs
We defined reported cost as the RVU‐based cost per item in the database. We then calculated the median across hospitals for each item in the database and set this as the standardized unit cost of that item at every hospital (Figure 1). Once standardized costs were assigned at the item level, we summed the costs of all items assigned to each patient and calculated the standardized cost of a hospitalization per patient at each hospital.
Examination of Cost Variation
We compared the standardized and reported costs of hospitalizations using medians, interquartile ranges, and interquartile ratios (Q75/Q25). To examine whether standardized costs can reduce the noise due to differences in overhead and other fixed costs, we calculated, for each hospital, the coefficients of variation (CV) for per‐day reported and standardized costs and per‐hospitalization reported and standardized costs. We used the Fligner‐Killeen test to determine whether the variance of CVs was different for reported and standardized costs.[19]
Creation of Basket of Goods
Because there can be differences in the costs of items, the number and types of items administered during hospitalizations, 2 hospitals with similar reported costs for a hospitalization might deliver different quantities and combinations of treatments (Figure 1). We wished to demonstrate that there is variation in reported costs of items when the quantity and type of item is held constant, so we created a basket of items. We chose items that are commonly administered to patients with heart failure, but could have chosen any combination of items. The basket included a day of medical room and board, a day of intensive care unit (ICU) room and board, a single dose of ‐blocker, a single dose of angiotensin‐converting enzyme inhibitor, complete blood count, a B‐natriuretic peptide level, a chest radiograph, a chest computed tomography, and an echocardiogram. We then examined the range of hospitals' reported costs for this basket of goods using percentiles, medians, and interquartile ranges.
Reported to Standardized Cost Ratio
Next, we calculated standardized costs of hospitalizations for included hospitals and examined the relationship between hospitals' mean reported costs and mean standardized costs. This ratio could help diagnose the mechanism of high reported costs for a hospital, because high reported costs with low utilization would indicate high fixed costs, while high reported costs with high utilization would indicate greater use of tests and treatments. We assigned hospitals to strata based on reported costs greater than standardized costs by more than 25%, reported costs within 25% of standardized costs, and reported costs less than standardized costs by more than 25%. We examined the association between hospital characteristics and strata using a 2 test. All analyses were carried out using SAS version 9.3 (SAS Institute Inc., Cary, NC).
RESULTS
The 234 hospitals included in the analysis contributed a total of 165,647 hospitalizations, with the number of hospitalizations ranging from 33 to 2,772 hospitalizations per hospital (see Supporting Table 1 in the online version of this article). Most were located in urban areas (84%), and many were in the southern United States (42%). The median hospital reported cost per hospitalization was $6,535, with an interquartile range of $5,541 to $7,454. The median standardized cost per hospitalization was $6,602, with a range of $5,866 to $7,386. The interquartile ratio (Q75/Q25) of the reported costs of a hospitalization was 1.35. After costs were standardized, the interquartile ratio fell to 1.26, indicating that variation decreased. We found that the median hospital reported cost per day was $1,651, with an IQR of $1,400 to $1,933 (ratio 1.38), whereas the median standardized cost per day was $1,640, with an IQR of $1,511 to $1,812 (ratio 1.20).
There were more than 15,000 items (eg, treatments, tests, and supplies) that received a standardized charge code in our cohort. These were divided into 11 summary departments and 40 standard departments (see Supporting Table 2 in the online version of this article). We observed a high level of variation in the reported costs of individual items: the reported costs of a day of room and board in an ICU ranged from $773 at hospitals at the 10th percentile to $2,471 at the 90th percentile (Table 1.). The standardized cost of a day of ICU room and board was $1,577. We also observed variation in the reported costs of items across item categories. Although a day of medical room and board showed a 3‐fold difference between the 10th and 90th percentile, we observed a more than 10‐fold difference in the reported cost of an echocardiogram, from $31 at the 10th percentile to $356 at the 90th percentile. After examining the hospital‐level cost for a basket of goods, we found variation in the reported costs for these items across hospitals, with a 10th percentile cost of $1,552 and a 90th percentile cost of $3,967.
| Reported Costs | 10th Percentile | 25th Percentile | 75th Percentile | 90th Percentile | Median (Standardized Cost) |
|---|---|---|---|---|---|
| |||||
| Item | |||||
| Day of medical | 490.03 | 586.41 | 889.95 | 1121.20 | 722.59 |
| Day of ICU | 773.01 | 1275.84 | 1994.81 | 2471.75 | 1577.93 |
| Complete blood count | 6.87 | 9.34 | 18.34 | 23.46 | 13.07 |
| B‐natriuretic peptide | 12.13 | 19.22 | 44.19 | 60.56 | 28.23 |
| Metoprolol | 0.20 | 0.68 | 2.67 | 3.74 | 1.66 |
| Lisinopril | 0.28 | 1.02 | 2.79 | 4.06 | 1.72 |
| Spironolactone | 0.22 | 0.53 | 2.68 | 3.83 | 1.63 |
| Furosemide | 1.27 | 2.45 | 5.73 | 8.12 | 3.82 |
| Chest x‐ray | 43.88 | 51.54 | 89.96 | 117.16 | 67.45 |
| Echocardiogram | 31.53 | 98.63 | 244.63 | 356.50 | 159.07 |
| Chest CT (w & w/o contrast) | 65.17 | 83.99 | 157.23 | 239.27 | 110.76 |
| Noninvasive positive pressure ventilation | 126.23 | 127.25 | 370.44 | 514.67 | 177.24 |
| Electrocardiogram | 12.08 | 18.77 | 42.74 | 64.94 | 29.78 |
| Total basket | 1552.50 | 2157.85 | 3417.34 | 3967.78 | 2710.49 |
We found that 46 (20%) hospitals had reported costs of hospitalizations that were 25% greater than standardized costs (Figure 2). This group of hospitals had overestimated reported costs of utilization; 146 (62%) had reported costs within 25% of standardized costs, and 42 (17%) had reported costs that were 25% less than standardized costs (indicating that reported costs underestimated utilization). We examined the relationship between hospital characteristics and strata and found no significant association between the reported to standardized cost ratio and number of beds, teaching status, or urban location (Table 2). Hospitals in the Midwest and South were more likely to have a lower reported cost of hospitalizations, whereas hospitals in the West were more likely to have higher reported costs (P<0.001). When using the CV to compare reported costs to standardized costs, we found that per‐day standardized costs showed reduced variance (P=0.0238), but there was no significant difference in variance of the reported and standardized costs when examining the entire hospitalization (P=0.1423). At the level of the hospitalization, the Spearman correlation coefficient between reported and standardized cost was 0.89.
| Reported Greater Than Standardized by >25%, n (%) | Reported Within 25% (2‐tailed) of Standardized, n (%) | Reported Less Than Standardized by >25%, n (%) | P for 2 Test | |
|---|---|---|---|---|
| Total | 46 (19.7) | 146 (62.4) | 42 (17.0) | |
| No. of beds | 0.2313 | |||
| <200 | 19 (41.3) | 40 (27.4) | 12 (28.6) | |
| 200400 | 14 (30.4) | 67 (45.9) | 15 (35.7) | |
| >400 | 13 (28.3) | 39 (26.7) | 15 (35.7) | |
| Teaching | 0.8278 | |||
| Yes | 13 (28.3) | 45 (30.8) | 11 (26.2) | |
| No | 33 (71.7) | 101 (69.2) | 31 (73.8) | |
| Region | <0.0001 | |||
| Midwest | 7 (15.2) | 43 (29.5) | 19 (45.2) | |
| Northeast | 6 (13.0) | 18 (12.3) | 3 (7.1) | |
| South | 14 (30.4) | 64 (43.8) | 20 (47.6) | |
| West | 19 (41.3) | 21 (14.4) | 0 (0) | |
| Urban vs rural | 36 (78.3) | 128 (87.7) | 33 (78.6) | 0.1703 |
To better understand how hospitals can achieve high reported costs through different mechanisms, we more closely examined 3 hospitals with similar reported costs (Figure 3). These hospitals represented low, average, and high utilization according to their standardized costs, but had similar average per‐hospitalization reported costs: $11,643, $11,787, and $11,892, respectively. The corresponding standardized costs were $8,757, $11,169, and $15,978. The hospital with high utilization ($15,978 in standardized costs) was accounted for by increased use of supplies and other services. In contrast, the low‐ and average‐utilization hospitals had proportionally lower standardized costs across categories, with the greatest percentage of spending going toward room and board (includes nursing).
DISCUSSION
In a large national sample of hospitals, we observed variation in the reported costs for a uniform basket of goods, with a more than 2‐fold difference in cost between the 10th and 90th percentile hospitals. These findings suggest that reported costs have limited ability to reliably describe differences in utilization across hospitals. In contrast, when we applied standardized costs, the variance of per‐day costs decreased significantly, and the interquartile ratio of per‐day and hospitalization costs decreased as well, suggesting less variation in utilization across hospitals than would have been inferred from a comparison of reported costs. Applying a single, standard cost to all items can facilitate comparisons of utilization between hospitals (Figure 1). Standardized costs will give hospitals the potential to compare their utilization to their competitors and will facilitate research that examines the comparative effectiveness of high and low utilization in the management of medical and surgical conditions.
The reported to standardized cost ratio is another useful tool. It indicates whether the hospital's reported costs exaggerate its utilization relative to other hospitals. In this study, we found that a significant proportion of hospitals (20%) had reported costs that exceeded standardized costs by more than 25%. These hospitals have higher infrastructure, labor, or acquisition costs relative to their peers. To the extent that these hospitals might wish to lower the cost of care at their institution, they could focus on renegotiating purchasing or labor contracts, identifying areas where they may be overstaffed, or holding off on future infrastructure investments (Table 3).[14] In contrast, 17% of hospitals had reported costs that were 25% less than standardized costs. High‐cost hospitals in this group are therefore providing more treatments and testing to patients relative to their peers and could focus cost‐control efforts on reducing unnecessary utilization and duplicative testing.[20] Our examination of the hospital with high reported costs and very high utilization revealed a high percentage of supplies and other items, which is a category used primarily for nursing expenditures (Figure 3). Because the use of nursing services is directly related to days spent in the hospital, this hospital may wish to more closely examine specific strategies for reducing length of stay.
| High Reported Costs/High Standardized Costs | High Reported Costs/Low Standardized Costs | Low Reported Costs/High Standardized Costs | Low Reported Costs/Low Standardized Costs | |
|---|---|---|---|---|
| Utilization | High | Low | High | Low |
| Severity of illness | Likely to be higher | Likely to be lower | Likely to be higher | Likely to be lower |
| Practice style | Likely to be more intense | Likely to be less intense | Likely to be more intense | Likely to be less intense |
| Fixed costs | High or average | High | Low | Low |
| Infrastructure costs | Likely to be higher | Likely to be higher | Likely to be lower | Likely to be lower |
| Labor costs | Likely to be higher | Likely to be higher | Likely to be lower | Likely to be lower |
| Reported‐to‐standardized cost ratio | Close to 1 | >1 | <1 | Close to 1 |
| Causes of high costs | High utilization, high fixed costs, or both | High acquisition costs, high labor costs, or expensive infrastructure | High utilization | |
| Interventions to reduce costs | Work with clinicians to alter practice style, consider renegotiating cost of acquisitions, hold off on new infrastructure investments | Consider renegotiating cost of acquisitions, hold off on new infrastructure investments, consider reducing size of labor force | Work with clinicians to alter practice style | |
| Usefulness of reported‐ to‐standardized cost ratio | Less useful | More useful | More useful | Less useful |
We did not find a consistent association between the reported to standardized cost ratio and hospital characteristics. This is an important finding that contradicts prior work examining associations between hospital characteristics and costs for heart failure patients,[21] further indicating the complexity of the relationship between fixed costs and variable costs and the difficulty in adjusting reported costs to calculate utilization. For example, small hospitals may have higher acquisition costs and more supply chain difficulties, but they may also have less technology, lower overhead costs, and fewer specialists to order tests and procedures. Hospital characteristics, such as urban location and teaching status, are commonly used as adjustors in cost studies because hospitals in urban areas with teaching missions (which often provide care to low‐income populations) are assumed to have higher fixed costs,[3, 4, 5, 6] but the lack of a consistent relationship between these characteristics and the standardized cost ratio may indicate that using these factors as adjustors for cost may not be effective and could even obscure differences in utilization between hospitals. Notably, we did find an association between hospital region and the reported to standardized cost ratio, but we hesitate to draw conclusions from this finding because the Premier database is imbalanced in terms of regional representation, with fewer hospitals in the Midwest and West and the bulk of the hospitals in the South.
Although standardized costs have great potential, this method has limitations as well. Standardized costs can only be applied when detailed billing data with item‐level costs are available. This is because calculation of standardized costs requires taking the median of item costs and applying the median cost across the database, maintaining the integrity of the relative cost of items to one another. The relative cost of items is preserved (ie, magnetic resonance imaging still costs more than an aspirin), which maintains the general scheme of RVU‐based costs while removing the noise of varying RVU‐based costs across hospitals.[7] Application of an arbitrary item cost would result in the loss of this relative cost difference. Because item costs are not available in traditional administrative datasets, these datasets would not be amenable to this method. However, highly detailed billing data are now being shared by hundreds of hospitals in the Premier network and the University Health System Consortium. These data are widely available to investigators, meaning that the generalizability of this method will only improve over time. It was also a limitation of the study that we chose a limited basket of items common to patients with heart failure to describe the range of reported costs and to provide a standardized snapshot by which to compare hospitals. Because we only included a few items, we may have overestimated or underestimated the range of reported costs for such a basket.
Standardized costs are a novel method for comparing utilization across hospitals. Used properly, they will help identify high‐ and low‐intensity providers of hospital care.
- Health care costs–a primer. Kaiser Family Foundation Web site. Available at: http://www.kff.org/insurance/7670.cfm. Accessed July 20, 2012.
- . Explaining high health care spending in the United States: an international comparison of supply, utilization, prices, and quality. The Commonwealth Fund. 2012. Available at: http://www.commonwealthfund.org/Publications/Issue‐Briefs/2012/May/High‐Health‐Care‐Spending. aspx. Accessed on July 20, 2012.
- , , , , , . The relationship between hospital spending and mortality in patients with sepsis. Arch Intern Med. 2011;171(4):292–299.
- , , , . The elusive connection between health care spending and quality. Health Aff (Millwood). 2009;28(1):w119–w123.
- , , , . Hospital quality and intensity of spending: is there an association? Health Aff (Millwood). 2009;28(4):w566–w572.
- , , , , . Measuring efficiency: the association of hospital costs and quality of care. Health Aff (Millwood). 2009;28(3):897–906.
- , . Assigning resources to health care use for health services research: options and consequences. Med Care. 2009;47(7 suppl 1):S70–S75.
- , , , , . Health care costing: data, methods, current applications. Med Care. 2009;47(7 suppl 1):S1–S6.
- . Determination of VA health care costs. Med Care Res Rev. 2003;60(3 suppl):124S–141S.
- . An improved set of standards for finding cost for cost‐effectiveness analysis. Med Care. 2009;47(7 suppl 1):S82–S88.
- , , , et al. Comparison of approaches for estimating prevalence costs of care for cancer patients: what is the impact of data source? Med Care. 2009;47(7 suppl 1):S64–S69.
- . Principles involved in costing. Med J Aust. 1990;153Suppl:S10–S12.
- . Spending more through “cost control:” our obsessive quest to gut the hospital. Health Aff (Millwood). 1996;15(2):145–154.
- , , , et al. Distribution of variable vs. fixed costs of hospital care. JAMA. 1999;281(7):644–649.
- . Administrative and claims records as sources of health care cost data. Med Care. 2009;47(7 suppl 1):S51–S55.
- , , , , , . Perioperative beta‐blocker therapy and mortality after major noncardiac surgery. N Engl J Med. 2005;353(4):349–361.
- , , , et al. Public reporting and pay for performance in hospital quality improvement. N Engl J Med. 2007;356(5):486–496.
- , , , et al. Procedure intensity and the cost of care. Circ Cardiovasc Qual Outcomes. 2012;5(3):308–313.
- , , . A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data. Technometrics. 1981;23:351–361.
- , , . Beyond the efficiency index: finding a better way to reduce overuse and increase efficiency in physician care. Health Aff (Millwood). 2008;27(4):w250–w259.
- , , . The association between hospital volume and processes, outcomes, and costs of care for congestive heart failure. Ann Intern Med. 2011;154(2):94–102.
- Health care costs–a primer. Kaiser Family Foundation Web site. Available at: http://www.kff.org/insurance/7670.cfm. Accessed July 20, 2012.
- . Explaining high health care spending in the United States: an international comparison of supply, utilization, prices, and quality. The Commonwealth Fund. 2012. Available at: http://www.commonwealthfund.org/Publications/Issue‐Briefs/2012/May/High‐Health‐Care‐Spending. aspx. Accessed on July 20, 2012.
- , , , , , . The relationship between hospital spending and mortality in patients with sepsis. Arch Intern Med. 2011;171(4):292–299.
- , , , . The elusive connection between health care spending and quality. Health Aff (Millwood). 2009;28(1):w119–w123.
- , , , . Hospital quality and intensity of spending: is there an association? Health Aff (Millwood). 2009;28(4):w566–w572.
- , , , , . Measuring efficiency: the association of hospital costs and quality of care. Health Aff (Millwood). 2009;28(3):897–906.
- , . Assigning resources to health care use for health services research: options and consequences. Med Care. 2009;47(7 suppl 1):S70–S75.
- , , , , . Health care costing: data, methods, current applications. Med Care. 2009;47(7 suppl 1):S1–S6.
- . Determination of VA health care costs. Med Care Res Rev. 2003;60(3 suppl):124S–141S.
- . An improved set of standards for finding cost for cost‐effectiveness analysis. Med Care. 2009;47(7 suppl 1):S82–S88.
- , , , et al. Comparison of approaches for estimating prevalence costs of care for cancer patients: what is the impact of data source? Med Care. 2009;47(7 suppl 1):S64–S69.
- . Principles involved in costing. Med J Aust. 1990;153Suppl:S10–S12.
- . Spending more through “cost control:” our obsessive quest to gut the hospital. Health Aff (Millwood). 1996;15(2):145–154.
- , , , et al. Distribution of variable vs. fixed costs of hospital care. JAMA. 1999;281(7):644–649.
- . Administrative and claims records as sources of health care cost data. Med Care. 2009;47(7 suppl 1):S51–S55.
- , , , , , . Perioperative beta‐blocker therapy and mortality after major noncardiac surgery. N Engl J Med. 2005;353(4):349–361.
- , , , et al. Public reporting and pay for performance in hospital quality improvement. N Engl J Med. 2007;356(5):486–496.
- , , , et al. Procedure intensity and the cost of care. Circ Cardiovasc Qual Outcomes. 2012;5(3):308–313.
- , , . A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data. Technometrics. 1981;23:351–361.
- , , . Beyond the efficiency index: finding a better way to reduce overuse and increase efficiency in physician care. Health Aff (Millwood). 2008;27(4):w250–w259.
- , , . The association between hospital volume and processes, outcomes, and costs of care for congestive heart failure. Ann Intern Med. 2011;154(2):94–102.
© 2013 Society of Hospital Medicine
