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Interhospital Transfer and Receipt of Specialty Procedures
Patients who undergo interhospital transfer (IHT) are felt to benefit from receipt of unique specialty care at the receiving hospital.1 Although only 1.5% of all hospitalized Medicare patients undergo hospital transfer,2 the frequency of transfer is much greater within certain patient populations, as may be expected with diagnoses requiring specialty care.3,4 Existent data demonstrate that 5% of Medicare patients admitted to the intensive care unit (ICU)5 and up to 50% of patients presenting with acute myocardial infarction (AMI) undergo IHT.6
More recent data suggest variability in hospital transfer practices not accounted for by differences in patient or hospital characteristics.2 Although disease-specific guidelines for IHT exist for certain diagnoses,3,4 the process remains largely nonstandardized for many patients,7 leading to ambiguity surrounding indications for transfer. Because limited data suggest worse outcomes for transferred versus nontransferred patients,8 a better understanding of the specialized care patients actually receive across the transfer continuum may help to elucidate potential indications for transfer and ultimately help delineate which patients are most (or least) likely to benefit from transfer and why.
In this national study, we examined a select cohort of transferred patients with diagnoses associated with specific specialty procedural services to determine if they received these procedures and where along the transfer continuum they were performed.
METHODS
We performed a cross-sectional analysis using the Center for Medicare and Medicaid Services 2013 100% Master Beneficiary Summary and Inpatient claims files. Our study protocol was approved by the Partners Healthcare Human Subjects Review Committee.
Beneficiaries were eligible for inclusion if they were aged ≥65 years, continuously enrolled in Medicare A and B, and with an acute care hospitalization claim in 2013, excluding Medicare managed care and end stage renal disease beneficiaries due to incomplete claims data in these groups. We additionally excluded beneficiaries hospitalized at federal or nonacute care hospitals, or critical access hospitals given their mission to stabilize and then transfer patients to referral hospitals.9
Transferred patients were defined as beneficiaries with corresponding “transfer in” and “transfer out” claims, or those with either claim and a corresponding date of admission/discharge from another hospital within 1 day of the claim, as we used in our prior research.2 Beneficiaries transferred to the same hospital, those with greater than 1 transfer within the same hospitalization, or those cared for at hospitals with “outlier” transfer-in rates equal to 100% or transfer-out rates greater than 35% were excluded from analysis given the suggestion of nonstandard claims practices.
We first identified the top 15 primary diagnoses at time of transfer using International Classification of Diseases, Ninth Revision (ICD-9) codes (supplementary Appendix), and then identified those 4 most likely to require specialty procedural services: AMI, gastrointestinal bleed (GI bleed), renal failure, and hip fracture/dislocation. We then chose associated ICD-9 procedure codes for each diagnosis, via expert opinion (authors SM and JS, hospitalist physicians with greater than 20 years of combined clinical experience), erring on overinclusion of procedure codes. We then quantified receipt of associated procedures at transferring and receiving hospitals, stratified by diagnosis.
We further explored the cohort of patients with hip fracture/dislocation who underwent an associated procedure at the transferring but not receiving hospital, examining the frequency with which these patients had other (nonrelated) procedures at the receiving hospital, and identifying which procedures they received.
RESULTS
Of the 101,507 patients transferred to another hospital, 19,613 (19.3%) had a primary diagnosis of AMI, GI bleed, renal failure, or hip fracture/dislocation. Table 1 lists the ICD-9 procedure codes associated with each diagnosis.
Distribution of receipt of specialty procedures at the transferring and receiving hospitals varied by disease (Figure). With the exception of GI bleed, patients more often received specialty procedural care at the receiving than the transferring hospital. Depending on primary diagnosis, between 32.4% and 89.1% of patients did not receive any associated specialty procedure at the receiving hospital.
Of the 370 (22.1%) hip fracture/dislocation patients that received a specialty procedure at the transferring but not receiving hospital, 132 (35.7%) did not receive any procedure at the receiving hospital, whereas the remaining 238 (64.3%) received an unrelated (not associated with the primary diagnosis) procedure. There was great variety in the types of procedures received, the most common being transfusion of blood products (ICD-9 Clinical Modification 9904).
DISCUSSION
Among transferred patients with primary diagnoses that have clearly associated specialized procedural services, we found that patients received these procedures at varying frequency and locations across the transfer continuum. Across 4 diagnoses, receipt of associated procedures was more common at the receiving than the transferring hospital, with the exception being patients with GI bleed. We additionally found that many transferred patients did not receive any associated specialty procedure at the receiving hospital. These findings suggest the strong likelihood of more diverse underlying reasons for transfer rather than solely receipt of specialized procedural care.
Despite the frequency with which AMI patients are transferred,6 and American Heart Association guidelines directing hospitals to transfer AMI patients to institutions able to provide necessary invasive treatments,4 prior studies suggest these patients inconsistently receive specialty intervention following transfer, including stress testing, cardiac catheterization, or coronary artery bypass graft surgery.10,11 Our findings add to these data, demonstrating that only 47.3% of patients transferred with AMI received any cardiac-related procedure at the receiving hospital. Additionally, we found that 38.1% of AMI patients do not receive any specialty procedures at either the transferring or the receiving hospital. Taken together, these data suggest possible discrepancies in the perceived need for these procedures between transferring and receiving hospitals, reasons for transfer related to these conditions that don’t involve an associated procedure, or reasons for transfer unrelated to specialty care of the primary diagnosis (such as care of comorbidities, hospital location, prior relationships with that hospital, or desire for a second opinion). Although some of these alternate reasons for transfer likely still benefit the patient, some of these reasons may not justify the increased risks of discontinuity of care created by IHT.
Given limited data looking at IHT practices for patients with other diagnoses, the varying patterns of specialty procedural interventions we observed among transferred patients with GI bleed, renal failure, and hip fracture/dislocation are novel contributions to this topic. Notably, we found that among patients transferred with a primary diagnosis of renal failure, the vast majority (84.1%) did not receive any associated procedure at either the transferring or the receiving hospital. It is possible that although these patients carried the diagnosis of renal failure, their clinical phenotype is more heterogeneous, and they could still be managed conservatively without receipt of invasive procedures such as hemodialysis.
Conversely, patients transferred with primary diagnosis of hip fracture/dislocation were far more likely to receive associated specialty procedural intervention at the receiving hospital, presumably reflective of the evidence demonstrating improved outcomes with early surgical intervention.12 However, these data do not explain the reasoning behind the substantial minority of patients who received specialty intervention at the transferring hospital prior to transfer or those that did not receive any specialty intervention at either the transferring or receiving hospital. Our secondary analysis demonstrating great variety in receipt and type of nonassociated procedures provided at the receiving hospital did not help to elucidate potential underlying reasons for transfer.
Notably, among patients transferred with primary diagnosis of GI bleed, receipt of specialty procedures was more common at the transferring (77.7%) than receiving (63.2%) hospital, with nearly half (49.3%) undergoing specialty procedures at both hospitals. It is possible that these findings are reflective of the broad array of specialty procedures examined within this diagnosis. For example, it is reasonable to consider that a patient may be stabilized with receipt of a blood transfusion at the transferring hospital, then transferred to undergo a diagnostic/therapeutic procedure (ie, endoscopy/colonoscopy) at the receiving hospital, as is suggested by our results.
Our study is subject to several limitations. First, given the criteria we used to define transfer, it is possible that we included nontransferred patients within our transferred cohort if they were discharged from one hospital and admitted to a different hospital within 1 day, although quality assurance analyses we conducted in prior studies on these data support the validity of the criteria used.2 Second, we cannot exclude the possibility that patients received nonprocedural specialty care (ie, expert opinion, specialized imaging, medical management, management of secondary diagnoses, etc.) not available at the transferring hospital, although, arguably, in select patients, such input could be obtained without physical transfer of the patient (ie, tele-consult). And even in patients transferred with intent to receive procedural care who did not ultimately receive that care, there is likely an appropriate “nonprocedure” rate, where patients who might benefit from a procedure receive a timely evaluation to reduce the risk of missing the opportunity to receive it. This would be analogous to transferring a patient to an ICU even if they do not end up requiring intubation or pressor therapy. However, given the likelihood of higher risks of IHT compared with intrahospital transfers, one could argue that the threshold of perceived benefit might be different in patients being considered for IHT. Additionally, we limited our analyses to only 4 diagnoses; thus, our findings may not be generalizable to other diagnoses of transferred patients. However, because the diagnoses we examined were ones considered most effectively treated with specialty procedural interventions, it is reasonable to presume that the variability in receipt of specialty procedures observed within these diagnoses is also present, if not greater, across other diagnoses. Third, although we intentionally included a broad array of specialty procedures associated with each diagnosis, it is possible that we overlooked particular specialty interventions. For example, in assuming that patients are most likely to be transferred to receive procedural services associated with their primary diagnosis, we may have missed alternate indications for transfer, including need for procedural care related to secondary or subsequent diagnoses (ie, a patient may have presented with GI bleed
CONCLUSIONS
We found that Medicare patients who undergo IHT with primary diagnoses of AMI, GI bleed, renal failure, and hip fracture/dislocation receive associated specialty interventions at varying frequency and locations, and many patients do not receive any associated procedures at receiving hospitals. Our findings suggest that specialty procedural care of patients, even those with primary diagnoses that often warrant specialized intervention, may not be the primary driver of IHT as commonly suggested, although underlying reasons for transfer in these and other “nonprocedural” transferred patients remains obscure. Given known ambiguity in the transfer process,7 and unclear benefit of IHT,8 additional research is required to further identify and evaluate other potential underlying reasons for transfer and to examine these in the context of patient outcomes, in order to understand which patients may or may not benefit from transfer and why.
Disclosure
The authors have nothing to disclose.
1. Iwashyna TJ. The incomplete infrastructure for interhospital patient transfer. Crit Care Med. 2012;40(8):2470-2478. PubMed
2. Mueller SK, Zheng J, Orav EJ, Schnipper JL. Rates, Predictors and Variability of Interhospital Transfers: A National Evaluation. J Hosp Med. 2017;12(6):435-442. PubMed
3. Guidelines for the transfer of critically ill patients. Guidelines Committee of the American College of Critical Care Medicine; Society of Critical Care Medicine and American Association of Critical-Care Nurses Transfer Guidelines Task Force. Crit Care Med. 1993;21(6):931-937. PubMed
4. Anderson JL, Adams CD, Antman EM, et al. 2011 ACCF/AHA Focused Update Incorporated Into the ACC/AHA 2007 Guidelines for the Management of Patients With Unstable Angina/Non-ST-Elevation Myocardial Infarction: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation. 2011;123(18):e426-e579. PubMed
5. Iwashyna TJ, Christie JD, Moody J, Kahn JM, Asch DA. The structure of critical care transfer networks. Med Care. 2009;47(7):787-793. PubMed
6. Iwashyna TJ, Kahn JM, Hayward RA, Nallamothu BK. Interhospital transfers among Medicare beneficiaries admitted for acute myocardial infarction at nonrevascularization hospitals. Circ Cardiovasc Qual Outcomes. 2010;3(5):468-475. PubMed
7. Bosk EA, Veinot T, Iwashyna TJ. Which patients and where: a qualitative study of patient transfers from community hospitals. Med Care. 2011;49(6):592-598. PubMed
8. Sokol-Hessner L, White AA, Davis KF, Herzig SJ, Hohmann SF. Interhospital transfer patients discharged by academic hospitalists and general internists: Characteristics and outcomes. J Hosp Med. 2016;11(4):245-250. PubMed
9. Department of Health and Human Services, Center for Medicare & Medicaid Services: Critical Access Hospitals. https://www.cms.gov/Outreach-and-Education/Medicare-Learning-Network-MLN/MLNProducts/downloads/CritAccessHospfctsht.pdf. Accessed June 29, 2017. PubMed
10. Roe MT, Chen AY, Delong ER, et al. Patterns of transfer for patients with non-ST-segment elevation acute coronary syndrome from community to tertiary care hospitals. Am Heart J. 2008;156(1):185-192. PubMed
11. Barreto-Filho JA, Wang Y, Rathore SS, et al. Transfer rates from nonprocedure hospitals after initial admission and outcomes among elderly patients with acute myocardial infarction. JAMA Intern Med. 2014;174(2):213-222. PubMed
12. Doruk H, Mas MR, Yildiz C, Sonmez A, Kyrdemir V. The effect of the timing of hip fracture surgery on the activity of daily living and mortality in elderly. Arch Gerontol Geriatr. 2004;39(2):179-185. PubMed
Patients who undergo interhospital transfer (IHT) are felt to benefit from receipt of unique specialty care at the receiving hospital.1 Although only 1.5% of all hospitalized Medicare patients undergo hospital transfer,2 the frequency of transfer is much greater within certain patient populations, as may be expected with diagnoses requiring specialty care.3,4 Existent data demonstrate that 5% of Medicare patients admitted to the intensive care unit (ICU)5 and up to 50% of patients presenting with acute myocardial infarction (AMI) undergo IHT.6
More recent data suggest variability in hospital transfer practices not accounted for by differences in patient or hospital characteristics.2 Although disease-specific guidelines for IHT exist for certain diagnoses,3,4 the process remains largely nonstandardized for many patients,7 leading to ambiguity surrounding indications for transfer. Because limited data suggest worse outcomes for transferred versus nontransferred patients,8 a better understanding of the specialized care patients actually receive across the transfer continuum may help to elucidate potential indications for transfer and ultimately help delineate which patients are most (or least) likely to benefit from transfer and why.
In this national study, we examined a select cohort of transferred patients with diagnoses associated with specific specialty procedural services to determine if they received these procedures and where along the transfer continuum they were performed.
METHODS
We performed a cross-sectional analysis using the Center for Medicare and Medicaid Services 2013 100% Master Beneficiary Summary and Inpatient claims files. Our study protocol was approved by the Partners Healthcare Human Subjects Review Committee.
Beneficiaries were eligible for inclusion if they were aged ≥65 years, continuously enrolled in Medicare A and B, and with an acute care hospitalization claim in 2013, excluding Medicare managed care and end stage renal disease beneficiaries due to incomplete claims data in these groups. We additionally excluded beneficiaries hospitalized at federal or nonacute care hospitals, or critical access hospitals given their mission to stabilize and then transfer patients to referral hospitals.9
Transferred patients were defined as beneficiaries with corresponding “transfer in” and “transfer out” claims, or those with either claim and a corresponding date of admission/discharge from another hospital within 1 day of the claim, as we used in our prior research.2 Beneficiaries transferred to the same hospital, those with greater than 1 transfer within the same hospitalization, or those cared for at hospitals with “outlier” transfer-in rates equal to 100% or transfer-out rates greater than 35% were excluded from analysis given the suggestion of nonstandard claims practices.
We first identified the top 15 primary diagnoses at time of transfer using International Classification of Diseases, Ninth Revision (ICD-9) codes (supplementary Appendix), and then identified those 4 most likely to require specialty procedural services: AMI, gastrointestinal bleed (GI bleed), renal failure, and hip fracture/dislocation. We then chose associated ICD-9 procedure codes for each diagnosis, via expert opinion (authors SM and JS, hospitalist physicians with greater than 20 years of combined clinical experience), erring on overinclusion of procedure codes. We then quantified receipt of associated procedures at transferring and receiving hospitals, stratified by diagnosis.
We further explored the cohort of patients with hip fracture/dislocation who underwent an associated procedure at the transferring but not receiving hospital, examining the frequency with which these patients had other (nonrelated) procedures at the receiving hospital, and identifying which procedures they received.
RESULTS
Of the 101,507 patients transferred to another hospital, 19,613 (19.3%) had a primary diagnosis of AMI, GI bleed, renal failure, or hip fracture/dislocation. Table 1 lists the ICD-9 procedure codes associated with each diagnosis.
Distribution of receipt of specialty procedures at the transferring and receiving hospitals varied by disease (Figure). With the exception of GI bleed, patients more often received specialty procedural care at the receiving than the transferring hospital. Depending on primary diagnosis, between 32.4% and 89.1% of patients did not receive any associated specialty procedure at the receiving hospital.
Of the 370 (22.1%) hip fracture/dislocation patients that received a specialty procedure at the transferring but not receiving hospital, 132 (35.7%) did not receive any procedure at the receiving hospital, whereas the remaining 238 (64.3%) received an unrelated (not associated with the primary diagnosis) procedure. There was great variety in the types of procedures received, the most common being transfusion of blood products (ICD-9 Clinical Modification 9904).
DISCUSSION
Among transferred patients with primary diagnoses that have clearly associated specialized procedural services, we found that patients received these procedures at varying frequency and locations across the transfer continuum. Across 4 diagnoses, receipt of associated procedures was more common at the receiving than the transferring hospital, with the exception being patients with GI bleed. We additionally found that many transferred patients did not receive any associated specialty procedure at the receiving hospital. These findings suggest the strong likelihood of more diverse underlying reasons for transfer rather than solely receipt of specialized procedural care.
Despite the frequency with which AMI patients are transferred,6 and American Heart Association guidelines directing hospitals to transfer AMI patients to institutions able to provide necessary invasive treatments,4 prior studies suggest these patients inconsistently receive specialty intervention following transfer, including stress testing, cardiac catheterization, or coronary artery bypass graft surgery.10,11 Our findings add to these data, demonstrating that only 47.3% of patients transferred with AMI received any cardiac-related procedure at the receiving hospital. Additionally, we found that 38.1% of AMI patients do not receive any specialty procedures at either the transferring or the receiving hospital. Taken together, these data suggest possible discrepancies in the perceived need for these procedures between transferring and receiving hospitals, reasons for transfer related to these conditions that don’t involve an associated procedure, or reasons for transfer unrelated to specialty care of the primary diagnosis (such as care of comorbidities, hospital location, prior relationships with that hospital, or desire for a second opinion). Although some of these alternate reasons for transfer likely still benefit the patient, some of these reasons may not justify the increased risks of discontinuity of care created by IHT.
Given limited data looking at IHT practices for patients with other diagnoses, the varying patterns of specialty procedural interventions we observed among transferred patients with GI bleed, renal failure, and hip fracture/dislocation are novel contributions to this topic. Notably, we found that among patients transferred with a primary diagnosis of renal failure, the vast majority (84.1%) did not receive any associated procedure at either the transferring or the receiving hospital. It is possible that although these patients carried the diagnosis of renal failure, their clinical phenotype is more heterogeneous, and they could still be managed conservatively without receipt of invasive procedures such as hemodialysis.
Conversely, patients transferred with primary diagnosis of hip fracture/dislocation were far more likely to receive associated specialty procedural intervention at the receiving hospital, presumably reflective of the evidence demonstrating improved outcomes with early surgical intervention.12 However, these data do not explain the reasoning behind the substantial minority of patients who received specialty intervention at the transferring hospital prior to transfer or those that did not receive any specialty intervention at either the transferring or receiving hospital. Our secondary analysis demonstrating great variety in receipt and type of nonassociated procedures provided at the receiving hospital did not help to elucidate potential underlying reasons for transfer.
Notably, among patients transferred with primary diagnosis of GI bleed, receipt of specialty procedures was more common at the transferring (77.7%) than receiving (63.2%) hospital, with nearly half (49.3%) undergoing specialty procedures at both hospitals. It is possible that these findings are reflective of the broad array of specialty procedures examined within this diagnosis. For example, it is reasonable to consider that a patient may be stabilized with receipt of a blood transfusion at the transferring hospital, then transferred to undergo a diagnostic/therapeutic procedure (ie, endoscopy/colonoscopy) at the receiving hospital, as is suggested by our results.
Our study is subject to several limitations. First, given the criteria we used to define transfer, it is possible that we included nontransferred patients within our transferred cohort if they were discharged from one hospital and admitted to a different hospital within 1 day, although quality assurance analyses we conducted in prior studies on these data support the validity of the criteria used.2 Second, we cannot exclude the possibility that patients received nonprocedural specialty care (ie, expert opinion, specialized imaging, medical management, management of secondary diagnoses, etc.) not available at the transferring hospital, although, arguably, in select patients, such input could be obtained without physical transfer of the patient (ie, tele-consult). And even in patients transferred with intent to receive procedural care who did not ultimately receive that care, there is likely an appropriate “nonprocedure” rate, where patients who might benefit from a procedure receive a timely evaluation to reduce the risk of missing the opportunity to receive it. This would be analogous to transferring a patient to an ICU even if they do not end up requiring intubation or pressor therapy. However, given the likelihood of higher risks of IHT compared with intrahospital transfers, one could argue that the threshold of perceived benefit might be different in patients being considered for IHT. Additionally, we limited our analyses to only 4 diagnoses; thus, our findings may not be generalizable to other diagnoses of transferred patients. However, because the diagnoses we examined were ones considered most effectively treated with specialty procedural interventions, it is reasonable to presume that the variability in receipt of specialty procedures observed within these diagnoses is also present, if not greater, across other diagnoses. Third, although we intentionally included a broad array of specialty procedures associated with each diagnosis, it is possible that we overlooked particular specialty interventions. For example, in assuming that patients are most likely to be transferred to receive procedural services associated with their primary diagnosis, we may have missed alternate indications for transfer, including need for procedural care related to secondary or subsequent diagnoses (ie, a patient may have presented with GI bleed
CONCLUSIONS
We found that Medicare patients who undergo IHT with primary diagnoses of AMI, GI bleed, renal failure, and hip fracture/dislocation receive associated specialty interventions at varying frequency and locations, and many patients do not receive any associated procedures at receiving hospitals. Our findings suggest that specialty procedural care of patients, even those with primary diagnoses that often warrant specialized intervention, may not be the primary driver of IHT as commonly suggested, although underlying reasons for transfer in these and other “nonprocedural” transferred patients remains obscure. Given known ambiguity in the transfer process,7 and unclear benefit of IHT,8 additional research is required to further identify and evaluate other potential underlying reasons for transfer and to examine these in the context of patient outcomes, in order to understand which patients may or may not benefit from transfer and why.
Disclosure
The authors have nothing to disclose.
Patients who undergo interhospital transfer (IHT) are felt to benefit from receipt of unique specialty care at the receiving hospital.1 Although only 1.5% of all hospitalized Medicare patients undergo hospital transfer,2 the frequency of transfer is much greater within certain patient populations, as may be expected with diagnoses requiring specialty care.3,4 Existent data demonstrate that 5% of Medicare patients admitted to the intensive care unit (ICU)5 and up to 50% of patients presenting with acute myocardial infarction (AMI) undergo IHT.6
More recent data suggest variability in hospital transfer practices not accounted for by differences in patient or hospital characteristics.2 Although disease-specific guidelines for IHT exist for certain diagnoses,3,4 the process remains largely nonstandardized for many patients,7 leading to ambiguity surrounding indications for transfer. Because limited data suggest worse outcomes for transferred versus nontransferred patients,8 a better understanding of the specialized care patients actually receive across the transfer continuum may help to elucidate potential indications for transfer and ultimately help delineate which patients are most (or least) likely to benefit from transfer and why.
In this national study, we examined a select cohort of transferred patients with diagnoses associated with specific specialty procedural services to determine if they received these procedures and where along the transfer continuum they were performed.
METHODS
We performed a cross-sectional analysis using the Center for Medicare and Medicaid Services 2013 100% Master Beneficiary Summary and Inpatient claims files. Our study protocol was approved by the Partners Healthcare Human Subjects Review Committee.
Beneficiaries were eligible for inclusion if they were aged ≥65 years, continuously enrolled in Medicare A and B, and with an acute care hospitalization claim in 2013, excluding Medicare managed care and end stage renal disease beneficiaries due to incomplete claims data in these groups. We additionally excluded beneficiaries hospitalized at federal or nonacute care hospitals, or critical access hospitals given their mission to stabilize and then transfer patients to referral hospitals.9
Transferred patients were defined as beneficiaries with corresponding “transfer in” and “transfer out” claims, or those with either claim and a corresponding date of admission/discharge from another hospital within 1 day of the claim, as we used in our prior research.2 Beneficiaries transferred to the same hospital, those with greater than 1 transfer within the same hospitalization, or those cared for at hospitals with “outlier” transfer-in rates equal to 100% or transfer-out rates greater than 35% were excluded from analysis given the suggestion of nonstandard claims practices.
We first identified the top 15 primary diagnoses at time of transfer using International Classification of Diseases, Ninth Revision (ICD-9) codes (supplementary Appendix), and then identified those 4 most likely to require specialty procedural services: AMI, gastrointestinal bleed (GI bleed), renal failure, and hip fracture/dislocation. We then chose associated ICD-9 procedure codes for each diagnosis, via expert opinion (authors SM and JS, hospitalist physicians with greater than 20 years of combined clinical experience), erring on overinclusion of procedure codes. We then quantified receipt of associated procedures at transferring and receiving hospitals, stratified by diagnosis.
We further explored the cohort of patients with hip fracture/dislocation who underwent an associated procedure at the transferring but not receiving hospital, examining the frequency with which these patients had other (nonrelated) procedures at the receiving hospital, and identifying which procedures they received.
RESULTS
Of the 101,507 patients transferred to another hospital, 19,613 (19.3%) had a primary diagnosis of AMI, GI bleed, renal failure, or hip fracture/dislocation. Table 1 lists the ICD-9 procedure codes associated with each diagnosis.
Distribution of receipt of specialty procedures at the transferring and receiving hospitals varied by disease (Figure). With the exception of GI bleed, patients more often received specialty procedural care at the receiving than the transferring hospital. Depending on primary diagnosis, between 32.4% and 89.1% of patients did not receive any associated specialty procedure at the receiving hospital.
Of the 370 (22.1%) hip fracture/dislocation patients that received a specialty procedure at the transferring but not receiving hospital, 132 (35.7%) did not receive any procedure at the receiving hospital, whereas the remaining 238 (64.3%) received an unrelated (not associated with the primary diagnosis) procedure. There was great variety in the types of procedures received, the most common being transfusion of blood products (ICD-9 Clinical Modification 9904).
DISCUSSION
Among transferred patients with primary diagnoses that have clearly associated specialized procedural services, we found that patients received these procedures at varying frequency and locations across the transfer continuum. Across 4 diagnoses, receipt of associated procedures was more common at the receiving than the transferring hospital, with the exception being patients with GI bleed. We additionally found that many transferred patients did not receive any associated specialty procedure at the receiving hospital. These findings suggest the strong likelihood of more diverse underlying reasons for transfer rather than solely receipt of specialized procedural care.
Despite the frequency with which AMI patients are transferred,6 and American Heart Association guidelines directing hospitals to transfer AMI patients to institutions able to provide necessary invasive treatments,4 prior studies suggest these patients inconsistently receive specialty intervention following transfer, including stress testing, cardiac catheterization, or coronary artery bypass graft surgery.10,11 Our findings add to these data, demonstrating that only 47.3% of patients transferred with AMI received any cardiac-related procedure at the receiving hospital. Additionally, we found that 38.1% of AMI patients do not receive any specialty procedures at either the transferring or the receiving hospital. Taken together, these data suggest possible discrepancies in the perceived need for these procedures between transferring and receiving hospitals, reasons for transfer related to these conditions that don’t involve an associated procedure, or reasons for transfer unrelated to specialty care of the primary diagnosis (such as care of comorbidities, hospital location, prior relationships with that hospital, or desire for a second opinion). Although some of these alternate reasons for transfer likely still benefit the patient, some of these reasons may not justify the increased risks of discontinuity of care created by IHT.
Given limited data looking at IHT practices for patients with other diagnoses, the varying patterns of specialty procedural interventions we observed among transferred patients with GI bleed, renal failure, and hip fracture/dislocation are novel contributions to this topic. Notably, we found that among patients transferred with a primary diagnosis of renal failure, the vast majority (84.1%) did not receive any associated procedure at either the transferring or the receiving hospital. It is possible that although these patients carried the diagnosis of renal failure, their clinical phenotype is more heterogeneous, and they could still be managed conservatively without receipt of invasive procedures such as hemodialysis.
Conversely, patients transferred with primary diagnosis of hip fracture/dislocation were far more likely to receive associated specialty procedural intervention at the receiving hospital, presumably reflective of the evidence demonstrating improved outcomes with early surgical intervention.12 However, these data do not explain the reasoning behind the substantial minority of patients who received specialty intervention at the transferring hospital prior to transfer or those that did not receive any specialty intervention at either the transferring or receiving hospital. Our secondary analysis demonstrating great variety in receipt and type of nonassociated procedures provided at the receiving hospital did not help to elucidate potential underlying reasons for transfer.
Notably, among patients transferred with primary diagnosis of GI bleed, receipt of specialty procedures was more common at the transferring (77.7%) than receiving (63.2%) hospital, with nearly half (49.3%) undergoing specialty procedures at both hospitals. It is possible that these findings are reflective of the broad array of specialty procedures examined within this diagnosis. For example, it is reasonable to consider that a patient may be stabilized with receipt of a blood transfusion at the transferring hospital, then transferred to undergo a diagnostic/therapeutic procedure (ie, endoscopy/colonoscopy) at the receiving hospital, as is suggested by our results.
Our study is subject to several limitations. First, given the criteria we used to define transfer, it is possible that we included nontransferred patients within our transferred cohort if they were discharged from one hospital and admitted to a different hospital within 1 day, although quality assurance analyses we conducted in prior studies on these data support the validity of the criteria used.2 Second, we cannot exclude the possibility that patients received nonprocedural specialty care (ie, expert opinion, specialized imaging, medical management, management of secondary diagnoses, etc.) not available at the transferring hospital, although, arguably, in select patients, such input could be obtained without physical transfer of the patient (ie, tele-consult). And even in patients transferred with intent to receive procedural care who did not ultimately receive that care, there is likely an appropriate “nonprocedure” rate, where patients who might benefit from a procedure receive a timely evaluation to reduce the risk of missing the opportunity to receive it. This would be analogous to transferring a patient to an ICU even if they do not end up requiring intubation or pressor therapy. However, given the likelihood of higher risks of IHT compared with intrahospital transfers, one could argue that the threshold of perceived benefit might be different in patients being considered for IHT. Additionally, we limited our analyses to only 4 diagnoses; thus, our findings may not be generalizable to other diagnoses of transferred patients. However, because the diagnoses we examined were ones considered most effectively treated with specialty procedural interventions, it is reasonable to presume that the variability in receipt of specialty procedures observed within these diagnoses is also present, if not greater, across other diagnoses. Third, although we intentionally included a broad array of specialty procedures associated with each diagnosis, it is possible that we overlooked particular specialty interventions. For example, in assuming that patients are most likely to be transferred to receive procedural services associated with their primary diagnosis, we may have missed alternate indications for transfer, including need for procedural care related to secondary or subsequent diagnoses (ie, a patient may have presented with GI bleed
CONCLUSIONS
We found that Medicare patients who undergo IHT with primary diagnoses of AMI, GI bleed, renal failure, and hip fracture/dislocation receive associated specialty interventions at varying frequency and locations, and many patients do not receive any associated procedures at receiving hospitals. Our findings suggest that specialty procedural care of patients, even those with primary diagnoses that often warrant specialized intervention, may not be the primary driver of IHT as commonly suggested, although underlying reasons for transfer in these and other “nonprocedural” transferred patients remains obscure. Given known ambiguity in the transfer process,7 and unclear benefit of IHT,8 additional research is required to further identify and evaluate other potential underlying reasons for transfer and to examine these in the context of patient outcomes, in order to understand which patients may or may not benefit from transfer and why.
Disclosure
The authors have nothing to disclose.
1. Iwashyna TJ. The incomplete infrastructure for interhospital patient transfer. Crit Care Med. 2012;40(8):2470-2478. PubMed
2. Mueller SK, Zheng J, Orav EJ, Schnipper JL. Rates, Predictors and Variability of Interhospital Transfers: A National Evaluation. J Hosp Med. 2017;12(6):435-442. PubMed
3. Guidelines for the transfer of critically ill patients. Guidelines Committee of the American College of Critical Care Medicine; Society of Critical Care Medicine and American Association of Critical-Care Nurses Transfer Guidelines Task Force. Crit Care Med. 1993;21(6):931-937. PubMed
4. Anderson JL, Adams CD, Antman EM, et al. 2011 ACCF/AHA Focused Update Incorporated Into the ACC/AHA 2007 Guidelines for the Management of Patients With Unstable Angina/Non-ST-Elevation Myocardial Infarction: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation. 2011;123(18):e426-e579. PubMed
5. Iwashyna TJ, Christie JD, Moody J, Kahn JM, Asch DA. The structure of critical care transfer networks. Med Care. 2009;47(7):787-793. PubMed
6. Iwashyna TJ, Kahn JM, Hayward RA, Nallamothu BK. Interhospital transfers among Medicare beneficiaries admitted for acute myocardial infarction at nonrevascularization hospitals. Circ Cardiovasc Qual Outcomes. 2010;3(5):468-475. PubMed
7. Bosk EA, Veinot T, Iwashyna TJ. Which patients and where: a qualitative study of patient transfers from community hospitals. Med Care. 2011;49(6):592-598. PubMed
8. Sokol-Hessner L, White AA, Davis KF, Herzig SJ, Hohmann SF. Interhospital transfer patients discharged by academic hospitalists and general internists: Characteristics and outcomes. J Hosp Med. 2016;11(4):245-250. PubMed
9. Department of Health and Human Services, Center for Medicare & Medicaid Services: Critical Access Hospitals. https://www.cms.gov/Outreach-and-Education/Medicare-Learning-Network-MLN/MLNProducts/downloads/CritAccessHospfctsht.pdf. Accessed June 29, 2017. PubMed
10. Roe MT, Chen AY, Delong ER, et al. Patterns of transfer for patients with non-ST-segment elevation acute coronary syndrome from community to tertiary care hospitals. Am Heart J. 2008;156(1):185-192. PubMed
11. Barreto-Filho JA, Wang Y, Rathore SS, et al. Transfer rates from nonprocedure hospitals after initial admission and outcomes among elderly patients with acute myocardial infarction. JAMA Intern Med. 2014;174(2):213-222. PubMed
12. Doruk H, Mas MR, Yildiz C, Sonmez A, Kyrdemir V. The effect of the timing of hip fracture surgery on the activity of daily living and mortality in elderly. Arch Gerontol Geriatr. 2004;39(2):179-185. PubMed
1. Iwashyna TJ. The incomplete infrastructure for interhospital patient transfer. Crit Care Med. 2012;40(8):2470-2478. PubMed
2. Mueller SK, Zheng J, Orav EJ, Schnipper JL. Rates, Predictors and Variability of Interhospital Transfers: A National Evaluation. J Hosp Med. 2017;12(6):435-442. PubMed
3. Guidelines for the transfer of critically ill patients. Guidelines Committee of the American College of Critical Care Medicine; Society of Critical Care Medicine and American Association of Critical-Care Nurses Transfer Guidelines Task Force. Crit Care Med. 1993;21(6):931-937. PubMed
4. Anderson JL, Adams CD, Antman EM, et al. 2011 ACCF/AHA Focused Update Incorporated Into the ACC/AHA 2007 Guidelines for the Management of Patients With Unstable Angina/Non-ST-Elevation Myocardial Infarction: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation. 2011;123(18):e426-e579. PubMed
5. Iwashyna TJ, Christie JD, Moody J, Kahn JM, Asch DA. The structure of critical care transfer networks. Med Care. 2009;47(7):787-793. PubMed
6. Iwashyna TJ, Kahn JM, Hayward RA, Nallamothu BK. Interhospital transfers among Medicare beneficiaries admitted for acute myocardial infarction at nonrevascularization hospitals. Circ Cardiovasc Qual Outcomes. 2010;3(5):468-475. PubMed
7. Bosk EA, Veinot T, Iwashyna TJ. Which patients and where: a qualitative study of patient transfers from community hospitals. Med Care. 2011;49(6):592-598. PubMed
8. Sokol-Hessner L, White AA, Davis KF, Herzig SJ, Hohmann SF. Interhospital transfer patients discharged by academic hospitalists and general internists: Characteristics and outcomes. J Hosp Med. 2016;11(4):245-250. PubMed
9. Department of Health and Human Services, Center for Medicare & Medicaid Services: Critical Access Hospitals. https://www.cms.gov/Outreach-and-Education/Medicare-Learning-Network-MLN/MLNProducts/downloads/CritAccessHospfctsht.pdf. Accessed June 29, 2017. PubMed
10. Roe MT, Chen AY, Delong ER, et al. Patterns of transfer for patients with non-ST-segment elevation acute coronary syndrome from community to tertiary care hospitals. Am Heart J. 2008;156(1):185-192. PubMed
11. Barreto-Filho JA, Wang Y, Rathore SS, et al. Transfer rates from nonprocedure hospitals after initial admission and outcomes among elderly patients with acute myocardial infarction. JAMA Intern Med. 2014;174(2):213-222. PubMed
12. Doruk H, Mas MR, Yildiz C, Sonmez A, Kyrdemir V. The effect of the timing of hip fracture surgery on the activity of daily living and mortality in elderly. Arch Gerontol Geriatr. 2004;39(2):179-185. PubMed
© 2017 Society of Hospital Medicine
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Rates, predictors and variability of interhospital transfers: A national evaluation
Interhospital transfer (IHT) is defined as the transfer of hospitalized patients between acute care hospitals. Although cited reasons for transfer include providing patients access to unique specialty services,1 patterns and practices of IHT remain largely unstudied. Interhospital transfer is known to be common in certain patient populations, including selected patients presenting to the intensive care unit2 and those with acute myocardial infarction (AMI),3-5 but no recent studies have looked at frequency of IHT among a broader group of hospitalized patients nationally. Little is known about which patients are selected for transfer and why.6 Limited evidence suggests poor concordance between cited reason for transfer among patients, transferring physicians, and receiving physicians,7 indicating ambiguity in this care process.
Interhospital transfer exposes patients to the potential risks associated with discontinuity of care. Communication is particularly vulnerable to error during times of transition.8-10 Patients transferred between acute care hospitals are especially vulnerable, given the severity of illness in this patient population,11 and the absence of other factors to fill in gaps in communication, such as common electronic health records. Limited existing literature suggests transferred patients use more resources 12-13 and experience worse outcomes compared to nontransferred patients,11 although these data involved limited patient populations, and adjustment for illness severity and other factors was variably addressed.14-16
To improve the quality and safety of IHT, therefore, it is necessary to understand which patients benefit from IHT and identify best practices in the IHT process.17 A fundamental first step is to study patterns and practices of IHT, in particular with an eye towards identifying unwarranted variation.18 This is important to understand the prevalence of the issue, provide possible evidence of lack of standardization, and natural experiments with which to identify best practices.
To address this, we conducted a foundational study examining a national sample of Medicare patients to determine the nationwide frequency of IHT among elderly patients, patient and hospital-level predictors of transfer, and hospital variability in IHT practices.
METHODS
We performed a cross-sectional analysis using 2 nationally representative datasets: (1) Center for Medicare and Medicaid Services (CMS) 2013 100% Master Beneficiary Summary and Inpatient claims files, which contains data on all fee-for-service program Medicare enrollees’ demographic information, date of death, and hospitalization claims, including ICD-9 codes for diagnoses, diagnosis-related group (DRG), and dates of service; merged with (2) 2013 American Hospital Association (AHA) data,19 which contains hospital-level characteristics for all acute care hospitals in the U.S. Our study protocol was approved by the Partners Healthcare Human Subjects Review Committee.
Beneficiaries were eligible for inclusion if they were 65 years or older, continuously enrolled in Medicare A and B, with an acute care hospitalization claim in 2013, excluding Medicare managed care and end-stage renal disease (ESRD) beneficiaries. We additionally excluded beneficiaries hospitalized at federal or nonacute care hospitals, or critical access hospitals given their mission to stabilize and transfer patients to referral hospitals.20
Transferred patients were defined as: (1) beneficiaries with a “transfer out” claim and a corresponding “transfer in” claim at a different hospital; as well as (2) beneficiaries with a “transfer out” claim and a corresponding date of admission to another hospital within 1 day following the date of claim; and (3) beneficiaries with a “transfer in” claim and a corresponding date of discharge from another hospital within 1 day preceding the date of claim. Beneficiaries transferred to the same hospital, or cared for at hospitals with “outlier” transfer in rates equal to 100% or transfer out rates greater than 35%, were excluded from analysis given the suggestion of nonstandard claims practices. Beneficiaries with greater than 1 transfer within the same hospitalization were additionally excluded.
Patient Characteristics
Patient characteristics were obtained from the CMS data files and included: demographics (age, sex, race); DRG-weight, categorized into quartiles; primary diagnosis for the index hospitalization using ICD-9 codes; patient comorbidity using ICD-9 codes compiled into a CMS-Hierarchical Condition Category (HCC) risk score;21 presence of Medicaid co-insurance; number of hospitalizations in the past 12 months, categorized into 0, 1, 2-3, and 4 or more; season, defined as calendar quarters; and median income per household by census tract. These characteristics were chosen a priori given expert opinion in combination with prior research demonstrating association with IHT.11,22
Hospital Characteristics
Hospital characteristics were obtained from AHA data files and included hospitals’ size, categorized into small, medium, and large (less than 100, 100 to 399, 400 or more beds); geographic location; ownership; teaching status; setting (urban vs. rural); case mix index (CMI) for all patients cared for at the hospital; and presence of selected specialty services, including certified trauma center, medical intensive care unit, cardiac intensive care unit, cardiac surgery services, adult interventional cardiac catheterization, adult cardiac electrophysiology, and composite score of presence of 55 other specialty services (complete list in Appendix A). All characteristics were chosen a priori given expert opinion or relationship of characteristics with IHT, and prior research utilizing AHA data.23-24
Analysis
Descriptive statistics were used to evaluate the frequency of IHT, characteristics of transferred patients, and number of days to transfer. Patient and hospital characteristics of transferred vs. nontransferred patients were compared using chi-square analyses.
To analyze the effects of each patient and hospital characteristic on the odds of transfer, we used logistic regression models incorporating all patient and hospital characteristics, accounting for fixed effects for diagnosis, and utilizing generalized estimating equations (the GENMOD procedure in SAS statistical software, v 9.4; SAS Institute Inc., Cary, North Carolina) to account for the clustering of patients within hospitals.25 Indicator variables were created for missing covariate data and included in analyses when missing data accounted for greater than 10% of the total cohort.
To measure the variability in transfer rates between hospitals, we used a sequence of random effects logistic regression models. We first ran a model with no covariates, representing the unadjusted differences in transfer rates between hospitals. We then added patient characteristics to see if the unadjusted differences in IHT rates were explained by differences in patient characteristics between hospitals. Lastly, we added hospital characteristics to determine if these explained the remaining differences in transfer rates. Each of the 3 models provided a measure of between-hospital variability, reflecting the degree to which IHT rates differed between hospitals. Additionally, we used the intercept from the unadjusted model and the measure of between-hospital variability from each model to calculate the 95% confidence intervals, illustrating the range of IHT rates spanning 95% of all hospitals. We used those same numbers to calculate the 25th and 75th percentiles, illustrating the range of IHT rates for the middle half of hospitals.
RESULTS
Among 28 million eligible beneficiaries, 6.6 million had an acute care hospitalization to nonfederal, noncritical access hospitals, and 107,741 met our defined criteria for IHT. An additional 3790 beneficiaries were excluded for being transferred to the same facility, 416 beneficiaries (115 transferred, 301 nontransferred) were excluded as they were cared for at 1 of the 11 hospitals with “outlier” transfer in/out rates, and 2329 were excluded because they had more than 1 transfer during hospitalization. Thus, the final cohort consisted of 101,507 transferred (1.5%) and 6,625,474 nontransferred beneficiaries (Figure 1). Of the 101,507 transferred beneficiaries, 2799 (2.8%) were included more than once (ie, experienced more than 1 IHT on separate hospitalizations throughout the study period; the vast majority of these had 2 separate hospitalizations resulting in IHT). Characteristics of transferred and nontransferred beneficiaries are shown (Table 1).
Among transferred patients, the top 5 primary diagnoses at time of transfer included AMI (12.2%), congestive heart failure (CHF) (7.2%), sepsis (6.6%), arrhythmia (6.6%), and pneumonia (3.4%). Comorbid conditions most commonly present in transferred patients included CHF (52.6%), renal failure (51.8%), arrhythmia (49.8%), and chronic obstructive pulmonary disease (COPD; 37.0%). The most common day of transfer was day after admission (hospital day 2, 24.7%), with 75% of transferred patients transferred before hospital day 6 (Appendix B).
After adjusting for all other patient and hospital characteristics and clustering by hospital, the following variables were associated with greater odds of transfer: older age, male sex, nonblack race, non-Medicaid co-insurance, higher comorbidity (HCC score), lower DRG-weight, and fewer hospitalizations in the prior 12 months. Beneficiaries also had greater odds of transfer if initially hospitalized at smaller hospitals, nonteaching hospitals, public hospitals, at hospitals in the Northeast, those with fewer specialty services, and those with a low CMI (Table 2).
DISCUSSION
In this nationally representative study of 6.6 million Medicare beneficiaries, we found that 1.5% of patients were transferred between acute care facilities and were most often transferred prior to hospital day 6. Older age, male sex, nonblack race, higher medical comorbidity, lower DRG weight, and fewer recent hospitalizations were associated with greater odds of transfer. Initial hospitalization at smaller, nonteaching, public hospitals, with fewer specialty services were associated with greater odds of transfer, while higher CMI was associated with a lower odds of transfer. The most common comorbid conditions among transferred patients included CHF, renal failure, arrhythmia, and COPD; particularly notable was the very high prevalence of these conditions among transferred as compared with nontransferred patients. Importantly, we found significant variation in IHT by region and a large variation in transfer practices by hospital, with significant variability in transfer rates even after accounting for known patient and hospital characteristics.
Among our examined population, we found that a sizable number of patients undergo IHT—more than 100,000 per year. Primary diagnoses at time of transfer consist of common inpatient conditions, including AMI, CHF, sepsis, arrhythmia, and pneumonia. Limited prior data support our findings, with up to 50% of AMI patients reportedly undergoing IHT,3-5 and severe sepsis and respiratory illness reported as common diagnoses at transfer.11 Although knowledge of these primary diagnoses does not directly confer an understanding of reason for transfer, one can speculate based on our findings. For example, research demonstrates the majority of AMI patients who undergo IHT had further intervention, including stress testing, cardiac catheterization, and/or coronary artery bypass graft surgery.5,26 Thus, it is reasonable to presume that many of the beneficiaries transferred with AMI were transferred to receive this more specialized cardiac care. We further found the majority of patients are transferred prior to hospital day 6 with the highest prevalence on day 2, supporting the hypothesis that these patients may be transferred for receipt of specialty services for their admission diagnosis. However, we cannot prove this presumption, and for other conditions, such as pneumonia, the plan after IHT is less obvious. There are numerous possible reasons for transfer,1 including patient preference and prior affiliation with receiving hospital. Further research is required to more fully define these reasons in greater detail.
We additionally found that certain patient characteristics were associated with greater odds of transfer. Research suggests that transferred patients are “sicker” than nontransferred patients.1,11 Although our findings in part confirm these data, we paradoxically found that higher DRG-weight and 4 or more hospitalizations in the past year were actually associated with lower odds of transfer. In addition, the oldest patients in our cohort (85 years or older) were actually less likely to be transferred than their slightly younger counterparts (75 to 84 years). These variables may reflect extreme illness or frailty,27 and providers consciously (or subconsciously) may factor this in to their decision to transfer, considering a threshold past which transfer would confer more risk than benefit (eg, a patient may be “too sick” for transfer). Indeed, in a secondary analysis without hospital characteristics or comorbidities, and with fixed effects by hospital, we found the highest rates of IHT in patients in the middle 2 quartiles of DRG-weight, supporting this threshold hypothesis. It is also possible that patients with numerous hospitalizations may be less likely to be transferred because of familiarity and a strong sense of responsibility to continue to care for those patients (although we cannot confirm that those prior hospitalizations were all with the same index hospital).
It is also notable that odds of transfer differed by race, with black patients 17% less likely to undergo transfer compared to whites, similar to findings in other IHT studies.11 This finding, in combination with our demonstration that Medicaid patients also have lower odds of transfer, warrants further investigation to ensure the process of IHT does not bias against these populations, as with other well-documented health disparities.28-30
The hospital predictors of transfer were largely expected. However, interestingly, when we controlled for all other patient and hospital characteristics, regional variation persisted, with highest odds of transfer with hospitalization in the Northeast, indicating variability by region not explained by other factors, and findings supported by other limited data.31 This variability was further elucidated in our examination of change in variance estimates accounting for patient, then hospital, characteristics. Although we expected and found marked variability in hospital transfer rates in our null model (without accounting for any patient or hospital characteristics), we interestingly found that variability increased upon adjusting for patient characteristics. This result is presumably due to the fact that patients who are more likely to be transferred (ie, “sick” patients) are more often already at hospitals less likely to transfer patients, supported by our findings that hospital CMI is inversely associated with odds of transfer (in other words, hospitals that care for a less sick patient population are more likely to transfer their patients, and hospitals that care for a sicker patient population [higher CMI] are less likely to transfer). Adjusting solely for patient characteristics effectively equalizes these patients across hospitals, which would lead to even increased variability in transfer rates. Conversely, when we then adjusted for hospital characteristics, variability in hospital transfer rates decreased by 83% (in other words, hospital characteristics, rather than patient characteristics, explained much of the variability in transfer rates), although significant unexplained variability remained. We should note that although the observed reduction in variability was explained by the patient and hospital characteristics included in the model, these characteristics do not necessarily justify the variability they accounted for; although patients’ race or hospitals’ location may explain some of the observed variability, this does not reasonably justify it.
This observed variability in transfer practices is not surprising given the absence of standardization and clear guidelines to direct clinical IHT practice.17 Selection of patients that may benefit from transfer is often ambiguous and subjective.6 The Emergency Medical Treatment and Active Labor Act laws dictate that hospitals transfer patients requiring a more specialized service, or when “medical benefits ... outweigh the increased risks to the individual...,” although in practice this provides little guidance to practitioners.1 Thus, clearer guidelines may be necessary to achieve less variable practices.
Our study is subject to several limitations. First, although nationally representative, the Medicare population is not reflective of all hospitalized patients nationwide. Additionally, we excluded patients transferred from the emergency room. Thus, the total number of patients who undergo IHT nationally is expected to be much higher than reflected in our analysis. We also excluded patients who were transferred more than once during a given hospitalization. This enabled us to focus on the initial transfer decision but does not allow us to look at patients who are transferred to a referral center and then transferred back. Second, given the criteria we used to define transfer, it is possible that we included nontransferred patients within our transferred cohort if they were discharged from one hospital and admitted to a different hospital within 1 day. However, on quality assurance analyses where we limited our cohort to only those beneficiaries with corresponding “transfer in” and “transfer out” claims (87% of the total cohort), we found no marked differences in our results. Additionally, although we assume that patient transfer status was coded correctly within the Medicare dataset, we could not confirm by individually examining each patient we defined as “transferred.” However, on additional quality assurance analyses where we examined randomly selected excluded patients with greater than 1 transfer during hospitalization, we found differing provider numbers with each transfer, suggesting validity of the coding. Third, because there are likely many unmeasured patient confounders, we cannot be sure how much of the between-hospital variation is due to incomplete adjustment for patient characteristics. However, since adjusting for patient characteristics actually increased variability in hospital transfer rates, it is unlikely that residual patient confounders fully explain our observed results. Despite this, other variables that are not available within the CMS or AHA datasets may further elucidate hospital transfer practices, including variables reflective of the transfer process (eg, time of day of patient transfer, time delay between initiation of transfer and patient arrival at accepting hospital, accepting service on transfer, etc.); other markers of illness severity (eg, clinical service at the time of index admission, acute physiology score, utilization of critical care services on arrival at receiving hospital); and other hospital system variables (ie, membership in an accountable care organization and/or regional care network, the density of nearby tertiary referral centers (indicating possible supply-induced demand), other variables reflective of the “transfer culture” (such as the transfer rate at the hospital or region where the attending physician trained, etc.). Lastly, though our examination provides important foundational information regarding IHT nationally, this study did not examine patient outcomes in transferred and nontransferred patients, which may help to determine which patients benefit (or do not benefit) from transfer and why. Further investigation is needed to study these outcomes.
CONCLUSION
In this national study of IHT, we found that a sizable number of patients admitted to the hospital undergo transfer to another acute care facility. Patients are transferred with common medical conditions, including those requiring specialized care such as AMI, and a high rate of comorbid clinical conditions, and certain patient and hospital characteristics are associated with greater odds of transfer. Although many of the observed associations between characteristics and odds of transfer were expected based on limited existing literature, we found several unexpected findings, eg, suggesting the possibility of a threshold beyond which sicker patients are not transferred. Additionally, we found that black and Medicaid patients had lower odds of transfer, which warrants further investigation for potential health care disparity. Importantly, we found much variability in the practice of IHT, as evidenced by the inexplicable differences in transfer by hospital region, and by residual unexplained variability in hospital transfer rates after accounting for patient and hospital characteristics, which may be due to lack of standard guidelines to direct IHT practices. In conclusion, this study of hospitalized Medicare patients provides important foundational information regarding rates and predictors of IHT nationally, as well as unexplained variability that exists within this complex care transition. Further investigation will be essential to understand reasons for, processes related to, and outcomes of transferred patients, to help guide standardization in best practices in care.
Disclosure
Nothing to report.
1. Iwashyna TJ. The incomplete infrastructure for interhospital patient transfer. Crit Care Med. 2012;40(8):2470-2478. PubMed
2. Iwashyna TJ, Christie JD, Moody J, Kahn JM, Asch DA. The structure of critical care transfer networks. Med Care. 2009;47(7):787-793. PubMed
3. Mehta RH, Stalhandske EJ, McCargar PA, Ruane TJ, Eagle KA. Elderly patients at highest risk with acute myocardial infarction are more frequently transferred from community hospitals to tertiary centers: reality or myth? Am Heart J. 1999;138(4 Pt 1):688-695. PubMed
4. Iwashyna TJ, Kahn JM, Hayward RA, Nallamothu BK. Interhospital transfers among Medicare beneficiaries admitted for acute myocardial infarction at nonrevascularization hospitals. Circ Cardiovasc Qual Outcomes. 2010;3(5):468-475. PubMed
5. Roe MT, Chen AY, Delong ER, Boden WE, Calvin JE Jr, Cairns CB, et al. Patterns of transfer for patients with non-ST-segment elevation acute coronary syndrome from community to tertiary care hospitals. Am Heart J. 2008;156(1):185-192. PubMed
6. Bosk EA, Veinot T, Iwashyna TJ. Which patients and where: a qualitative study of patient transfers from community hospitals. Med Care. 2011;49(6):592-598. PubMed
7. Wagner J, Iwashyna TJ, Kahn JM. Reasons underlying interhospital transfers to an academic medical intensive care unit. J Crit Care. 2013;28(2):202-208. PubMed
8. Cohen MD, Hilligoss PB. The published literature on handoffs in hospitals: deficiencies identified in an extensive review. Qual Saf Health Care. 2010;19(6):493-497. PubMed
9. Riesenberg LA, Leitzsch J, Massucci JL, et al. Residents’ and attending physicians’ handoffs: a systematic review of the literature. Acad Med. 2009;84(12):1775-1787. PubMed
10. Arora V, Johnson J, Lovinger D, Humphrey HJ, Meltzer DO. Communication failures in patient sign-out and suggestions for improvement: a critical incident analysis. Qual Saf Health Care. 2005;14(6):401-407. PubMed
11. Sokol-Hessner L, White AA, Davis KF, Herzig SJ, Hohmann SF. Interhospital transfer patients discharged by academic hospitalists and general internists: characteristics and outcomes. J Hosp Med. 2016;11(4):245-250. PubMed
12. Bernard AM, Hayward RA, Rosevear J, Chun H, McMahon LF. Comparing the hospitalizations of transfer and non-transfer patients in an academic medical center. Acad Med. 1996;71(3):262-266. PubMed
13. Golestanian E, Scruggs JE, Gangnon RE, Mak RP, Wood KE. Effect of interhospital transfer on resource utilization and outcomes at a tertiary care referral center. Crit Care Med. 2007;35(6):1470-1476. PubMed
14. Durairaj L, Will JG, Torner JC, Doebbeling BN. Prognostic factors for mortality following interhospital transfers to the medical intensive care unit of a tertiary referral center. Crit Care Med. 2003;31(7):1981-1986. PubMed
15. Kerr HD, Byrd JC. Community hospital transfers to a VA Medical Center. JAMA. 1989;262(1):70-73. PubMed
16. Dragsted L, Jörgensen J, Jensen NH, et al. Interhospital comparisons of patient outcome from intensive care: importance of lead-time bias. Crit Care Med. 1989;17(5):418-422. PubMed
17. Gupta K, Mueller SK. Interhospital transfers: the need for standards. J Hosp Med. 2015;10(6):415-417. PubMed
18. The Dartmouth Atlas of Health Care: Understanding of the Efficiency and Effectiveness of the Health Care System. The Dartmouth Institute for Health Practice and Clinical Policy, Lebanon, NH. http://www.dartmouthatlas.org/. Accessed November 1, 2016.
19. American Hospital Association Annual Survey Database. American Hospital Association, Chicago, IL. http://www.ahadataviewer.com/book-cd-products/AHA-Survey/. Accessed July 1, 2013.
20. U.S. Department of Health and Human Services (HRSA): What are critical access hospitals (CAH)? http://www.hrsa.gov/healthit/toolbox/RuralHealthITtoolbox/Introduction/critical.html. Accessed June 9, 2016.
21. Li P, Kim MM, Doshi JA. Comparison of the performance of the CMS Hierarchical Condition Category (CMS-HCC) risk adjuster with the Charlson and Elixhauser comorbidity measures in predicting mortality. BMC Health Serv Res. 2010;10:245. PubMed
22. Hernandez-Boussard T, Davies S, McDonald K, Wang NE. Interhospital facility transfers in the United States: a nationwide outcomes study. J Patient Saf. Nov 13 2014. PubMed
23. Landon BE, Normand SL, Lessler A, et al. Quality of care for the treatment of acute medical conditions in US hospitals. Arch Intern Med. 2006;166(22):2511-2517. PubMed
24. Mueller SK, Lipsitz S, Hicks LS. Impact of hospital teaching intensity on quality of care and patient outcomes. Med Care.2013;51(7):567-574. PubMed
25. Lopez L, Hicks LS, Cohen AP, McKean S, Weissman JS. Hospitalists and the quality of care in hospitals. Arch Intern Med. 2009;169(15):1389-1394. PubMed
26. Barreto-Filho JA, Wang Y, Rathore SS, et al. Transfer rates from nonprocedure hospitals after initial admission and outcomes among elderly patients with acute myocardial infarction. JAMA Intern Med. 2014;174(2):213-222. PubMed
27. Carlson JE, Zocchi KA, Bettencourt DM, et al. Measuring frailty in the hospitalized elderly: concept of functional homeostasis. Am J Phys Med Rehabil. 1998;77(3):252-257. PubMed
28. Ward E, Jemal A, Cokkinides V, et al. Cancer disparities by race/ethnicity and socioeconomic status. CA Cancer J Clin. 2004;54(2):78-93. PubMed
29. Iribarren C, Tolstykh I, Somkin CP, et al. Sex and racial/ethnic disparities in outcomes after acute myocardial infarction: a cohort study among members of a large integrated health care delivery system in northern California. Arch Intern Med. 2005;165(18):2105-2113. PubMed
30. Kawachi I, Daniels N, Robinson DE. Health disparities by race and class: why both matter. Health Aff (Millwood). 2005;24(2):343-352. PubMed
31. Herrigel DJ, Carroll M, Fanning C, Steinberg MB, Parikh A, Usher M. Interhospital transfer handoff practices among US tertiary care centers: a descriptive survey. J Hosp Med. 2016;11(6):413-417. PubMed
Interhospital transfer (IHT) is defined as the transfer of hospitalized patients between acute care hospitals. Although cited reasons for transfer include providing patients access to unique specialty services,1 patterns and practices of IHT remain largely unstudied. Interhospital transfer is known to be common in certain patient populations, including selected patients presenting to the intensive care unit2 and those with acute myocardial infarction (AMI),3-5 but no recent studies have looked at frequency of IHT among a broader group of hospitalized patients nationally. Little is known about which patients are selected for transfer and why.6 Limited evidence suggests poor concordance between cited reason for transfer among patients, transferring physicians, and receiving physicians,7 indicating ambiguity in this care process.
Interhospital transfer exposes patients to the potential risks associated with discontinuity of care. Communication is particularly vulnerable to error during times of transition.8-10 Patients transferred between acute care hospitals are especially vulnerable, given the severity of illness in this patient population,11 and the absence of other factors to fill in gaps in communication, such as common electronic health records. Limited existing literature suggests transferred patients use more resources 12-13 and experience worse outcomes compared to nontransferred patients,11 although these data involved limited patient populations, and adjustment for illness severity and other factors was variably addressed.14-16
To improve the quality and safety of IHT, therefore, it is necessary to understand which patients benefit from IHT and identify best practices in the IHT process.17 A fundamental first step is to study patterns and practices of IHT, in particular with an eye towards identifying unwarranted variation.18 This is important to understand the prevalence of the issue, provide possible evidence of lack of standardization, and natural experiments with which to identify best practices.
To address this, we conducted a foundational study examining a national sample of Medicare patients to determine the nationwide frequency of IHT among elderly patients, patient and hospital-level predictors of transfer, and hospital variability in IHT practices.
METHODS
We performed a cross-sectional analysis using 2 nationally representative datasets: (1) Center for Medicare and Medicaid Services (CMS) 2013 100% Master Beneficiary Summary and Inpatient claims files, which contains data on all fee-for-service program Medicare enrollees’ demographic information, date of death, and hospitalization claims, including ICD-9 codes for diagnoses, diagnosis-related group (DRG), and dates of service; merged with (2) 2013 American Hospital Association (AHA) data,19 which contains hospital-level characteristics for all acute care hospitals in the U.S. Our study protocol was approved by the Partners Healthcare Human Subjects Review Committee.
Beneficiaries were eligible for inclusion if they were 65 years or older, continuously enrolled in Medicare A and B, with an acute care hospitalization claim in 2013, excluding Medicare managed care and end-stage renal disease (ESRD) beneficiaries. We additionally excluded beneficiaries hospitalized at federal or nonacute care hospitals, or critical access hospitals given their mission to stabilize and transfer patients to referral hospitals.20
Transferred patients were defined as: (1) beneficiaries with a “transfer out” claim and a corresponding “transfer in” claim at a different hospital; as well as (2) beneficiaries with a “transfer out” claim and a corresponding date of admission to another hospital within 1 day following the date of claim; and (3) beneficiaries with a “transfer in” claim and a corresponding date of discharge from another hospital within 1 day preceding the date of claim. Beneficiaries transferred to the same hospital, or cared for at hospitals with “outlier” transfer in rates equal to 100% or transfer out rates greater than 35%, were excluded from analysis given the suggestion of nonstandard claims practices. Beneficiaries with greater than 1 transfer within the same hospitalization were additionally excluded.
Patient Characteristics
Patient characteristics were obtained from the CMS data files and included: demographics (age, sex, race); DRG-weight, categorized into quartiles; primary diagnosis for the index hospitalization using ICD-9 codes; patient comorbidity using ICD-9 codes compiled into a CMS-Hierarchical Condition Category (HCC) risk score;21 presence of Medicaid co-insurance; number of hospitalizations in the past 12 months, categorized into 0, 1, 2-3, and 4 or more; season, defined as calendar quarters; and median income per household by census tract. These characteristics were chosen a priori given expert opinion in combination with prior research demonstrating association with IHT.11,22
Hospital Characteristics
Hospital characteristics were obtained from AHA data files and included hospitals’ size, categorized into small, medium, and large (less than 100, 100 to 399, 400 or more beds); geographic location; ownership; teaching status; setting (urban vs. rural); case mix index (CMI) for all patients cared for at the hospital; and presence of selected specialty services, including certified trauma center, medical intensive care unit, cardiac intensive care unit, cardiac surgery services, adult interventional cardiac catheterization, adult cardiac electrophysiology, and composite score of presence of 55 other specialty services (complete list in Appendix A). All characteristics were chosen a priori given expert opinion or relationship of characteristics with IHT, and prior research utilizing AHA data.23-24
Analysis
Descriptive statistics were used to evaluate the frequency of IHT, characteristics of transferred patients, and number of days to transfer. Patient and hospital characteristics of transferred vs. nontransferred patients were compared using chi-square analyses.
To analyze the effects of each patient and hospital characteristic on the odds of transfer, we used logistic regression models incorporating all patient and hospital characteristics, accounting for fixed effects for diagnosis, and utilizing generalized estimating equations (the GENMOD procedure in SAS statistical software, v 9.4; SAS Institute Inc., Cary, North Carolina) to account for the clustering of patients within hospitals.25 Indicator variables were created for missing covariate data and included in analyses when missing data accounted for greater than 10% of the total cohort.
To measure the variability in transfer rates between hospitals, we used a sequence of random effects logistic regression models. We first ran a model with no covariates, representing the unadjusted differences in transfer rates between hospitals. We then added patient characteristics to see if the unadjusted differences in IHT rates were explained by differences in patient characteristics between hospitals. Lastly, we added hospital characteristics to determine if these explained the remaining differences in transfer rates. Each of the 3 models provided a measure of between-hospital variability, reflecting the degree to which IHT rates differed between hospitals. Additionally, we used the intercept from the unadjusted model and the measure of between-hospital variability from each model to calculate the 95% confidence intervals, illustrating the range of IHT rates spanning 95% of all hospitals. We used those same numbers to calculate the 25th and 75th percentiles, illustrating the range of IHT rates for the middle half of hospitals.
RESULTS
Among 28 million eligible beneficiaries, 6.6 million had an acute care hospitalization to nonfederal, noncritical access hospitals, and 107,741 met our defined criteria for IHT. An additional 3790 beneficiaries were excluded for being transferred to the same facility, 416 beneficiaries (115 transferred, 301 nontransferred) were excluded as they were cared for at 1 of the 11 hospitals with “outlier” transfer in/out rates, and 2329 were excluded because they had more than 1 transfer during hospitalization. Thus, the final cohort consisted of 101,507 transferred (1.5%) and 6,625,474 nontransferred beneficiaries (Figure 1). Of the 101,507 transferred beneficiaries, 2799 (2.8%) were included more than once (ie, experienced more than 1 IHT on separate hospitalizations throughout the study period; the vast majority of these had 2 separate hospitalizations resulting in IHT). Characteristics of transferred and nontransferred beneficiaries are shown (Table 1).
Among transferred patients, the top 5 primary diagnoses at time of transfer included AMI (12.2%), congestive heart failure (CHF) (7.2%), sepsis (6.6%), arrhythmia (6.6%), and pneumonia (3.4%). Comorbid conditions most commonly present in transferred patients included CHF (52.6%), renal failure (51.8%), arrhythmia (49.8%), and chronic obstructive pulmonary disease (COPD; 37.0%). The most common day of transfer was day after admission (hospital day 2, 24.7%), with 75% of transferred patients transferred before hospital day 6 (Appendix B).
After adjusting for all other patient and hospital characteristics and clustering by hospital, the following variables were associated with greater odds of transfer: older age, male sex, nonblack race, non-Medicaid co-insurance, higher comorbidity (HCC score), lower DRG-weight, and fewer hospitalizations in the prior 12 months. Beneficiaries also had greater odds of transfer if initially hospitalized at smaller hospitals, nonteaching hospitals, public hospitals, at hospitals in the Northeast, those with fewer specialty services, and those with a low CMI (Table 2).
DISCUSSION
In this nationally representative study of 6.6 million Medicare beneficiaries, we found that 1.5% of patients were transferred between acute care facilities and were most often transferred prior to hospital day 6. Older age, male sex, nonblack race, higher medical comorbidity, lower DRG weight, and fewer recent hospitalizations were associated with greater odds of transfer. Initial hospitalization at smaller, nonteaching, public hospitals, with fewer specialty services were associated with greater odds of transfer, while higher CMI was associated with a lower odds of transfer. The most common comorbid conditions among transferred patients included CHF, renal failure, arrhythmia, and COPD; particularly notable was the very high prevalence of these conditions among transferred as compared with nontransferred patients. Importantly, we found significant variation in IHT by region and a large variation in transfer practices by hospital, with significant variability in transfer rates even after accounting for known patient and hospital characteristics.
Among our examined population, we found that a sizable number of patients undergo IHT—more than 100,000 per year. Primary diagnoses at time of transfer consist of common inpatient conditions, including AMI, CHF, sepsis, arrhythmia, and pneumonia. Limited prior data support our findings, with up to 50% of AMI patients reportedly undergoing IHT,3-5 and severe sepsis and respiratory illness reported as common diagnoses at transfer.11 Although knowledge of these primary diagnoses does not directly confer an understanding of reason for transfer, one can speculate based on our findings. For example, research demonstrates the majority of AMI patients who undergo IHT had further intervention, including stress testing, cardiac catheterization, and/or coronary artery bypass graft surgery.5,26 Thus, it is reasonable to presume that many of the beneficiaries transferred with AMI were transferred to receive this more specialized cardiac care. We further found the majority of patients are transferred prior to hospital day 6 with the highest prevalence on day 2, supporting the hypothesis that these patients may be transferred for receipt of specialty services for their admission diagnosis. However, we cannot prove this presumption, and for other conditions, such as pneumonia, the plan after IHT is less obvious. There are numerous possible reasons for transfer,1 including patient preference and prior affiliation with receiving hospital. Further research is required to more fully define these reasons in greater detail.
We additionally found that certain patient characteristics were associated with greater odds of transfer. Research suggests that transferred patients are “sicker” than nontransferred patients.1,11 Although our findings in part confirm these data, we paradoxically found that higher DRG-weight and 4 or more hospitalizations in the past year were actually associated with lower odds of transfer. In addition, the oldest patients in our cohort (85 years or older) were actually less likely to be transferred than their slightly younger counterparts (75 to 84 years). These variables may reflect extreme illness or frailty,27 and providers consciously (or subconsciously) may factor this in to their decision to transfer, considering a threshold past which transfer would confer more risk than benefit (eg, a patient may be “too sick” for transfer). Indeed, in a secondary analysis without hospital characteristics or comorbidities, and with fixed effects by hospital, we found the highest rates of IHT in patients in the middle 2 quartiles of DRG-weight, supporting this threshold hypothesis. It is also possible that patients with numerous hospitalizations may be less likely to be transferred because of familiarity and a strong sense of responsibility to continue to care for those patients (although we cannot confirm that those prior hospitalizations were all with the same index hospital).
It is also notable that odds of transfer differed by race, with black patients 17% less likely to undergo transfer compared to whites, similar to findings in other IHT studies.11 This finding, in combination with our demonstration that Medicaid patients also have lower odds of transfer, warrants further investigation to ensure the process of IHT does not bias against these populations, as with other well-documented health disparities.28-30
The hospital predictors of transfer were largely expected. However, interestingly, when we controlled for all other patient and hospital characteristics, regional variation persisted, with highest odds of transfer with hospitalization in the Northeast, indicating variability by region not explained by other factors, and findings supported by other limited data.31 This variability was further elucidated in our examination of change in variance estimates accounting for patient, then hospital, characteristics. Although we expected and found marked variability in hospital transfer rates in our null model (without accounting for any patient or hospital characteristics), we interestingly found that variability increased upon adjusting for patient characteristics. This result is presumably due to the fact that patients who are more likely to be transferred (ie, “sick” patients) are more often already at hospitals less likely to transfer patients, supported by our findings that hospital CMI is inversely associated with odds of transfer (in other words, hospitals that care for a less sick patient population are more likely to transfer their patients, and hospitals that care for a sicker patient population [higher CMI] are less likely to transfer). Adjusting solely for patient characteristics effectively equalizes these patients across hospitals, which would lead to even increased variability in transfer rates. Conversely, when we then adjusted for hospital characteristics, variability in hospital transfer rates decreased by 83% (in other words, hospital characteristics, rather than patient characteristics, explained much of the variability in transfer rates), although significant unexplained variability remained. We should note that although the observed reduction in variability was explained by the patient and hospital characteristics included in the model, these characteristics do not necessarily justify the variability they accounted for; although patients’ race or hospitals’ location may explain some of the observed variability, this does not reasonably justify it.
This observed variability in transfer practices is not surprising given the absence of standardization and clear guidelines to direct clinical IHT practice.17 Selection of patients that may benefit from transfer is often ambiguous and subjective.6 The Emergency Medical Treatment and Active Labor Act laws dictate that hospitals transfer patients requiring a more specialized service, or when “medical benefits ... outweigh the increased risks to the individual...,” although in practice this provides little guidance to practitioners.1 Thus, clearer guidelines may be necessary to achieve less variable practices.
Our study is subject to several limitations. First, although nationally representative, the Medicare population is not reflective of all hospitalized patients nationwide. Additionally, we excluded patients transferred from the emergency room. Thus, the total number of patients who undergo IHT nationally is expected to be much higher than reflected in our analysis. We also excluded patients who were transferred more than once during a given hospitalization. This enabled us to focus on the initial transfer decision but does not allow us to look at patients who are transferred to a referral center and then transferred back. Second, given the criteria we used to define transfer, it is possible that we included nontransferred patients within our transferred cohort if they were discharged from one hospital and admitted to a different hospital within 1 day. However, on quality assurance analyses where we limited our cohort to only those beneficiaries with corresponding “transfer in” and “transfer out” claims (87% of the total cohort), we found no marked differences in our results. Additionally, although we assume that patient transfer status was coded correctly within the Medicare dataset, we could not confirm by individually examining each patient we defined as “transferred.” However, on additional quality assurance analyses where we examined randomly selected excluded patients with greater than 1 transfer during hospitalization, we found differing provider numbers with each transfer, suggesting validity of the coding. Third, because there are likely many unmeasured patient confounders, we cannot be sure how much of the between-hospital variation is due to incomplete adjustment for patient characteristics. However, since adjusting for patient characteristics actually increased variability in hospital transfer rates, it is unlikely that residual patient confounders fully explain our observed results. Despite this, other variables that are not available within the CMS or AHA datasets may further elucidate hospital transfer practices, including variables reflective of the transfer process (eg, time of day of patient transfer, time delay between initiation of transfer and patient arrival at accepting hospital, accepting service on transfer, etc.); other markers of illness severity (eg, clinical service at the time of index admission, acute physiology score, utilization of critical care services on arrival at receiving hospital); and other hospital system variables (ie, membership in an accountable care organization and/or regional care network, the density of nearby tertiary referral centers (indicating possible supply-induced demand), other variables reflective of the “transfer culture” (such as the transfer rate at the hospital or region where the attending physician trained, etc.). Lastly, though our examination provides important foundational information regarding IHT nationally, this study did not examine patient outcomes in transferred and nontransferred patients, which may help to determine which patients benefit (or do not benefit) from transfer and why. Further investigation is needed to study these outcomes.
CONCLUSION
In this national study of IHT, we found that a sizable number of patients admitted to the hospital undergo transfer to another acute care facility. Patients are transferred with common medical conditions, including those requiring specialized care such as AMI, and a high rate of comorbid clinical conditions, and certain patient and hospital characteristics are associated with greater odds of transfer. Although many of the observed associations between characteristics and odds of transfer were expected based on limited existing literature, we found several unexpected findings, eg, suggesting the possibility of a threshold beyond which sicker patients are not transferred. Additionally, we found that black and Medicaid patients had lower odds of transfer, which warrants further investigation for potential health care disparity. Importantly, we found much variability in the practice of IHT, as evidenced by the inexplicable differences in transfer by hospital region, and by residual unexplained variability in hospital transfer rates after accounting for patient and hospital characteristics, which may be due to lack of standard guidelines to direct IHT practices. In conclusion, this study of hospitalized Medicare patients provides important foundational information regarding rates and predictors of IHT nationally, as well as unexplained variability that exists within this complex care transition. Further investigation will be essential to understand reasons for, processes related to, and outcomes of transferred patients, to help guide standardization in best practices in care.
Disclosure
Nothing to report.
Interhospital transfer (IHT) is defined as the transfer of hospitalized patients between acute care hospitals. Although cited reasons for transfer include providing patients access to unique specialty services,1 patterns and practices of IHT remain largely unstudied. Interhospital transfer is known to be common in certain patient populations, including selected patients presenting to the intensive care unit2 and those with acute myocardial infarction (AMI),3-5 but no recent studies have looked at frequency of IHT among a broader group of hospitalized patients nationally. Little is known about which patients are selected for transfer and why.6 Limited evidence suggests poor concordance between cited reason for transfer among patients, transferring physicians, and receiving physicians,7 indicating ambiguity in this care process.
Interhospital transfer exposes patients to the potential risks associated with discontinuity of care. Communication is particularly vulnerable to error during times of transition.8-10 Patients transferred between acute care hospitals are especially vulnerable, given the severity of illness in this patient population,11 and the absence of other factors to fill in gaps in communication, such as common electronic health records. Limited existing literature suggests transferred patients use more resources 12-13 and experience worse outcomes compared to nontransferred patients,11 although these data involved limited patient populations, and adjustment for illness severity and other factors was variably addressed.14-16
To improve the quality and safety of IHT, therefore, it is necessary to understand which patients benefit from IHT and identify best practices in the IHT process.17 A fundamental first step is to study patterns and practices of IHT, in particular with an eye towards identifying unwarranted variation.18 This is important to understand the prevalence of the issue, provide possible evidence of lack of standardization, and natural experiments with which to identify best practices.
To address this, we conducted a foundational study examining a national sample of Medicare patients to determine the nationwide frequency of IHT among elderly patients, patient and hospital-level predictors of transfer, and hospital variability in IHT practices.
METHODS
We performed a cross-sectional analysis using 2 nationally representative datasets: (1) Center for Medicare and Medicaid Services (CMS) 2013 100% Master Beneficiary Summary and Inpatient claims files, which contains data on all fee-for-service program Medicare enrollees’ demographic information, date of death, and hospitalization claims, including ICD-9 codes for diagnoses, diagnosis-related group (DRG), and dates of service; merged with (2) 2013 American Hospital Association (AHA) data,19 which contains hospital-level characteristics for all acute care hospitals in the U.S. Our study protocol was approved by the Partners Healthcare Human Subjects Review Committee.
Beneficiaries were eligible for inclusion if they were 65 years or older, continuously enrolled in Medicare A and B, with an acute care hospitalization claim in 2013, excluding Medicare managed care and end-stage renal disease (ESRD) beneficiaries. We additionally excluded beneficiaries hospitalized at federal or nonacute care hospitals, or critical access hospitals given their mission to stabilize and transfer patients to referral hospitals.20
Transferred patients were defined as: (1) beneficiaries with a “transfer out” claim and a corresponding “transfer in” claim at a different hospital; as well as (2) beneficiaries with a “transfer out” claim and a corresponding date of admission to another hospital within 1 day following the date of claim; and (3) beneficiaries with a “transfer in” claim and a corresponding date of discharge from another hospital within 1 day preceding the date of claim. Beneficiaries transferred to the same hospital, or cared for at hospitals with “outlier” transfer in rates equal to 100% or transfer out rates greater than 35%, were excluded from analysis given the suggestion of nonstandard claims practices. Beneficiaries with greater than 1 transfer within the same hospitalization were additionally excluded.
Patient Characteristics
Patient characteristics were obtained from the CMS data files and included: demographics (age, sex, race); DRG-weight, categorized into quartiles; primary diagnosis for the index hospitalization using ICD-9 codes; patient comorbidity using ICD-9 codes compiled into a CMS-Hierarchical Condition Category (HCC) risk score;21 presence of Medicaid co-insurance; number of hospitalizations in the past 12 months, categorized into 0, 1, 2-3, and 4 or more; season, defined as calendar quarters; and median income per household by census tract. These characteristics were chosen a priori given expert opinion in combination with prior research demonstrating association with IHT.11,22
Hospital Characteristics
Hospital characteristics were obtained from AHA data files and included hospitals’ size, categorized into small, medium, and large (less than 100, 100 to 399, 400 or more beds); geographic location; ownership; teaching status; setting (urban vs. rural); case mix index (CMI) for all patients cared for at the hospital; and presence of selected specialty services, including certified trauma center, medical intensive care unit, cardiac intensive care unit, cardiac surgery services, adult interventional cardiac catheterization, adult cardiac electrophysiology, and composite score of presence of 55 other specialty services (complete list in Appendix A). All characteristics were chosen a priori given expert opinion or relationship of characteristics with IHT, and prior research utilizing AHA data.23-24
Analysis
Descriptive statistics were used to evaluate the frequency of IHT, characteristics of transferred patients, and number of days to transfer. Patient and hospital characteristics of transferred vs. nontransferred patients were compared using chi-square analyses.
To analyze the effects of each patient and hospital characteristic on the odds of transfer, we used logistic regression models incorporating all patient and hospital characteristics, accounting for fixed effects for diagnosis, and utilizing generalized estimating equations (the GENMOD procedure in SAS statistical software, v 9.4; SAS Institute Inc., Cary, North Carolina) to account for the clustering of patients within hospitals.25 Indicator variables were created for missing covariate data and included in analyses when missing data accounted for greater than 10% of the total cohort.
To measure the variability in transfer rates between hospitals, we used a sequence of random effects logistic regression models. We first ran a model with no covariates, representing the unadjusted differences in transfer rates between hospitals. We then added patient characteristics to see if the unadjusted differences in IHT rates were explained by differences in patient characteristics between hospitals. Lastly, we added hospital characteristics to determine if these explained the remaining differences in transfer rates. Each of the 3 models provided a measure of between-hospital variability, reflecting the degree to which IHT rates differed between hospitals. Additionally, we used the intercept from the unadjusted model and the measure of between-hospital variability from each model to calculate the 95% confidence intervals, illustrating the range of IHT rates spanning 95% of all hospitals. We used those same numbers to calculate the 25th and 75th percentiles, illustrating the range of IHT rates for the middle half of hospitals.
RESULTS
Among 28 million eligible beneficiaries, 6.6 million had an acute care hospitalization to nonfederal, noncritical access hospitals, and 107,741 met our defined criteria for IHT. An additional 3790 beneficiaries were excluded for being transferred to the same facility, 416 beneficiaries (115 transferred, 301 nontransferred) were excluded as they were cared for at 1 of the 11 hospitals with “outlier” transfer in/out rates, and 2329 were excluded because they had more than 1 transfer during hospitalization. Thus, the final cohort consisted of 101,507 transferred (1.5%) and 6,625,474 nontransferred beneficiaries (Figure 1). Of the 101,507 transferred beneficiaries, 2799 (2.8%) were included more than once (ie, experienced more than 1 IHT on separate hospitalizations throughout the study period; the vast majority of these had 2 separate hospitalizations resulting in IHT). Characteristics of transferred and nontransferred beneficiaries are shown (Table 1).
Among transferred patients, the top 5 primary diagnoses at time of transfer included AMI (12.2%), congestive heart failure (CHF) (7.2%), sepsis (6.6%), arrhythmia (6.6%), and pneumonia (3.4%). Comorbid conditions most commonly present in transferred patients included CHF (52.6%), renal failure (51.8%), arrhythmia (49.8%), and chronic obstructive pulmonary disease (COPD; 37.0%). The most common day of transfer was day after admission (hospital day 2, 24.7%), with 75% of transferred patients transferred before hospital day 6 (Appendix B).
After adjusting for all other patient and hospital characteristics and clustering by hospital, the following variables were associated with greater odds of transfer: older age, male sex, nonblack race, non-Medicaid co-insurance, higher comorbidity (HCC score), lower DRG-weight, and fewer hospitalizations in the prior 12 months. Beneficiaries also had greater odds of transfer if initially hospitalized at smaller hospitals, nonteaching hospitals, public hospitals, at hospitals in the Northeast, those with fewer specialty services, and those with a low CMI (Table 2).
DISCUSSION
In this nationally representative study of 6.6 million Medicare beneficiaries, we found that 1.5% of patients were transferred between acute care facilities and were most often transferred prior to hospital day 6. Older age, male sex, nonblack race, higher medical comorbidity, lower DRG weight, and fewer recent hospitalizations were associated with greater odds of transfer. Initial hospitalization at smaller, nonteaching, public hospitals, with fewer specialty services were associated with greater odds of transfer, while higher CMI was associated with a lower odds of transfer. The most common comorbid conditions among transferred patients included CHF, renal failure, arrhythmia, and COPD; particularly notable was the very high prevalence of these conditions among transferred as compared with nontransferred patients. Importantly, we found significant variation in IHT by region and a large variation in transfer practices by hospital, with significant variability in transfer rates even after accounting for known patient and hospital characteristics.
Among our examined population, we found that a sizable number of patients undergo IHT—more than 100,000 per year. Primary diagnoses at time of transfer consist of common inpatient conditions, including AMI, CHF, sepsis, arrhythmia, and pneumonia. Limited prior data support our findings, with up to 50% of AMI patients reportedly undergoing IHT,3-5 and severe sepsis and respiratory illness reported as common diagnoses at transfer.11 Although knowledge of these primary diagnoses does not directly confer an understanding of reason for transfer, one can speculate based on our findings. For example, research demonstrates the majority of AMI patients who undergo IHT had further intervention, including stress testing, cardiac catheterization, and/or coronary artery bypass graft surgery.5,26 Thus, it is reasonable to presume that many of the beneficiaries transferred with AMI were transferred to receive this more specialized cardiac care. We further found the majority of patients are transferred prior to hospital day 6 with the highest prevalence on day 2, supporting the hypothesis that these patients may be transferred for receipt of specialty services for their admission diagnosis. However, we cannot prove this presumption, and for other conditions, such as pneumonia, the plan after IHT is less obvious. There are numerous possible reasons for transfer,1 including patient preference and prior affiliation with receiving hospital. Further research is required to more fully define these reasons in greater detail.
We additionally found that certain patient characteristics were associated with greater odds of transfer. Research suggests that transferred patients are “sicker” than nontransferred patients.1,11 Although our findings in part confirm these data, we paradoxically found that higher DRG-weight and 4 or more hospitalizations in the past year were actually associated with lower odds of transfer. In addition, the oldest patients in our cohort (85 years or older) were actually less likely to be transferred than their slightly younger counterparts (75 to 84 years). These variables may reflect extreme illness or frailty,27 and providers consciously (or subconsciously) may factor this in to their decision to transfer, considering a threshold past which transfer would confer more risk than benefit (eg, a patient may be “too sick” for transfer). Indeed, in a secondary analysis without hospital characteristics or comorbidities, and with fixed effects by hospital, we found the highest rates of IHT in patients in the middle 2 quartiles of DRG-weight, supporting this threshold hypothesis. It is also possible that patients with numerous hospitalizations may be less likely to be transferred because of familiarity and a strong sense of responsibility to continue to care for those patients (although we cannot confirm that those prior hospitalizations were all with the same index hospital).
It is also notable that odds of transfer differed by race, with black patients 17% less likely to undergo transfer compared to whites, similar to findings in other IHT studies.11 This finding, in combination with our demonstration that Medicaid patients also have lower odds of transfer, warrants further investigation to ensure the process of IHT does not bias against these populations, as with other well-documented health disparities.28-30
The hospital predictors of transfer were largely expected. However, interestingly, when we controlled for all other patient and hospital characteristics, regional variation persisted, with highest odds of transfer with hospitalization in the Northeast, indicating variability by region not explained by other factors, and findings supported by other limited data.31 This variability was further elucidated in our examination of change in variance estimates accounting for patient, then hospital, characteristics. Although we expected and found marked variability in hospital transfer rates in our null model (without accounting for any patient or hospital characteristics), we interestingly found that variability increased upon adjusting for patient characteristics. This result is presumably due to the fact that patients who are more likely to be transferred (ie, “sick” patients) are more often already at hospitals less likely to transfer patients, supported by our findings that hospital CMI is inversely associated with odds of transfer (in other words, hospitals that care for a less sick patient population are more likely to transfer their patients, and hospitals that care for a sicker patient population [higher CMI] are less likely to transfer). Adjusting solely for patient characteristics effectively equalizes these patients across hospitals, which would lead to even increased variability in transfer rates. Conversely, when we then adjusted for hospital characteristics, variability in hospital transfer rates decreased by 83% (in other words, hospital characteristics, rather than patient characteristics, explained much of the variability in transfer rates), although significant unexplained variability remained. We should note that although the observed reduction in variability was explained by the patient and hospital characteristics included in the model, these characteristics do not necessarily justify the variability they accounted for; although patients’ race or hospitals’ location may explain some of the observed variability, this does not reasonably justify it.
This observed variability in transfer practices is not surprising given the absence of standardization and clear guidelines to direct clinical IHT practice.17 Selection of patients that may benefit from transfer is often ambiguous and subjective.6 The Emergency Medical Treatment and Active Labor Act laws dictate that hospitals transfer patients requiring a more specialized service, or when “medical benefits ... outweigh the increased risks to the individual...,” although in practice this provides little guidance to practitioners.1 Thus, clearer guidelines may be necessary to achieve less variable practices.
Our study is subject to several limitations. First, although nationally representative, the Medicare population is not reflective of all hospitalized patients nationwide. Additionally, we excluded patients transferred from the emergency room. Thus, the total number of patients who undergo IHT nationally is expected to be much higher than reflected in our analysis. We also excluded patients who were transferred more than once during a given hospitalization. This enabled us to focus on the initial transfer decision but does not allow us to look at patients who are transferred to a referral center and then transferred back. Second, given the criteria we used to define transfer, it is possible that we included nontransferred patients within our transferred cohort if they were discharged from one hospital and admitted to a different hospital within 1 day. However, on quality assurance analyses where we limited our cohort to only those beneficiaries with corresponding “transfer in” and “transfer out” claims (87% of the total cohort), we found no marked differences in our results. Additionally, although we assume that patient transfer status was coded correctly within the Medicare dataset, we could not confirm by individually examining each patient we defined as “transferred.” However, on additional quality assurance analyses where we examined randomly selected excluded patients with greater than 1 transfer during hospitalization, we found differing provider numbers with each transfer, suggesting validity of the coding. Third, because there are likely many unmeasured patient confounders, we cannot be sure how much of the between-hospital variation is due to incomplete adjustment for patient characteristics. However, since adjusting for patient characteristics actually increased variability in hospital transfer rates, it is unlikely that residual patient confounders fully explain our observed results. Despite this, other variables that are not available within the CMS or AHA datasets may further elucidate hospital transfer practices, including variables reflective of the transfer process (eg, time of day of patient transfer, time delay between initiation of transfer and patient arrival at accepting hospital, accepting service on transfer, etc.); other markers of illness severity (eg, clinical service at the time of index admission, acute physiology score, utilization of critical care services on arrival at receiving hospital); and other hospital system variables (ie, membership in an accountable care organization and/or regional care network, the density of nearby tertiary referral centers (indicating possible supply-induced demand), other variables reflective of the “transfer culture” (such as the transfer rate at the hospital or region where the attending physician trained, etc.). Lastly, though our examination provides important foundational information regarding IHT nationally, this study did not examine patient outcomes in transferred and nontransferred patients, which may help to determine which patients benefit (or do not benefit) from transfer and why. Further investigation is needed to study these outcomes.
CONCLUSION
In this national study of IHT, we found that a sizable number of patients admitted to the hospital undergo transfer to another acute care facility. Patients are transferred with common medical conditions, including those requiring specialized care such as AMI, and a high rate of comorbid clinical conditions, and certain patient and hospital characteristics are associated with greater odds of transfer. Although many of the observed associations between characteristics and odds of transfer were expected based on limited existing literature, we found several unexpected findings, eg, suggesting the possibility of a threshold beyond which sicker patients are not transferred. Additionally, we found that black and Medicaid patients had lower odds of transfer, which warrants further investigation for potential health care disparity. Importantly, we found much variability in the practice of IHT, as evidenced by the inexplicable differences in transfer by hospital region, and by residual unexplained variability in hospital transfer rates after accounting for patient and hospital characteristics, which may be due to lack of standard guidelines to direct IHT practices. In conclusion, this study of hospitalized Medicare patients provides important foundational information regarding rates and predictors of IHT nationally, as well as unexplained variability that exists within this complex care transition. Further investigation will be essential to understand reasons for, processes related to, and outcomes of transferred patients, to help guide standardization in best practices in care.
Disclosure
Nothing to report.
1. Iwashyna TJ. The incomplete infrastructure for interhospital patient transfer. Crit Care Med. 2012;40(8):2470-2478. PubMed
2. Iwashyna TJ, Christie JD, Moody J, Kahn JM, Asch DA. The structure of critical care transfer networks. Med Care. 2009;47(7):787-793. PubMed
3. Mehta RH, Stalhandske EJ, McCargar PA, Ruane TJ, Eagle KA. Elderly patients at highest risk with acute myocardial infarction are more frequently transferred from community hospitals to tertiary centers: reality or myth? Am Heart J. 1999;138(4 Pt 1):688-695. PubMed
4. Iwashyna TJ, Kahn JM, Hayward RA, Nallamothu BK. Interhospital transfers among Medicare beneficiaries admitted for acute myocardial infarction at nonrevascularization hospitals. Circ Cardiovasc Qual Outcomes. 2010;3(5):468-475. PubMed
5. Roe MT, Chen AY, Delong ER, Boden WE, Calvin JE Jr, Cairns CB, et al. Patterns of transfer for patients with non-ST-segment elevation acute coronary syndrome from community to tertiary care hospitals. Am Heart J. 2008;156(1):185-192. PubMed
6. Bosk EA, Veinot T, Iwashyna TJ. Which patients and where: a qualitative study of patient transfers from community hospitals. Med Care. 2011;49(6):592-598. PubMed
7. Wagner J, Iwashyna TJ, Kahn JM. Reasons underlying interhospital transfers to an academic medical intensive care unit. J Crit Care. 2013;28(2):202-208. PubMed
8. Cohen MD, Hilligoss PB. The published literature on handoffs in hospitals: deficiencies identified in an extensive review. Qual Saf Health Care. 2010;19(6):493-497. PubMed
9. Riesenberg LA, Leitzsch J, Massucci JL, et al. Residents’ and attending physicians’ handoffs: a systematic review of the literature. Acad Med. 2009;84(12):1775-1787. PubMed
10. Arora V, Johnson J, Lovinger D, Humphrey HJ, Meltzer DO. Communication failures in patient sign-out and suggestions for improvement: a critical incident analysis. Qual Saf Health Care. 2005;14(6):401-407. PubMed
11. Sokol-Hessner L, White AA, Davis KF, Herzig SJ, Hohmann SF. Interhospital transfer patients discharged by academic hospitalists and general internists: characteristics and outcomes. J Hosp Med. 2016;11(4):245-250. PubMed
12. Bernard AM, Hayward RA, Rosevear J, Chun H, McMahon LF. Comparing the hospitalizations of transfer and non-transfer patients in an academic medical center. Acad Med. 1996;71(3):262-266. PubMed
13. Golestanian E, Scruggs JE, Gangnon RE, Mak RP, Wood KE. Effect of interhospital transfer on resource utilization and outcomes at a tertiary care referral center. Crit Care Med. 2007;35(6):1470-1476. PubMed
14. Durairaj L, Will JG, Torner JC, Doebbeling BN. Prognostic factors for mortality following interhospital transfers to the medical intensive care unit of a tertiary referral center. Crit Care Med. 2003;31(7):1981-1986. PubMed
15. Kerr HD, Byrd JC. Community hospital transfers to a VA Medical Center. JAMA. 1989;262(1):70-73. PubMed
16. Dragsted L, Jörgensen J, Jensen NH, et al. Interhospital comparisons of patient outcome from intensive care: importance of lead-time bias. Crit Care Med. 1989;17(5):418-422. PubMed
17. Gupta K, Mueller SK. Interhospital transfers: the need for standards. J Hosp Med. 2015;10(6):415-417. PubMed
18. The Dartmouth Atlas of Health Care: Understanding of the Efficiency and Effectiveness of the Health Care System. The Dartmouth Institute for Health Practice and Clinical Policy, Lebanon, NH. http://www.dartmouthatlas.org/. Accessed November 1, 2016.
19. American Hospital Association Annual Survey Database. American Hospital Association, Chicago, IL. http://www.ahadataviewer.com/book-cd-products/AHA-Survey/. Accessed July 1, 2013.
20. U.S. Department of Health and Human Services (HRSA): What are critical access hospitals (CAH)? http://www.hrsa.gov/healthit/toolbox/RuralHealthITtoolbox/Introduction/critical.html. Accessed June 9, 2016.
21. Li P, Kim MM, Doshi JA. Comparison of the performance of the CMS Hierarchical Condition Category (CMS-HCC) risk adjuster with the Charlson and Elixhauser comorbidity measures in predicting mortality. BMC Health Serv Res. 2010;10:245. PubMed
22. Hernandez-Boussard T, Davies S, McDonald K, Wang NE. Interhospital facility transfers in the United States: a nationwide outcomes study. J Patient Saf. Nov 13 2014. PubMed
23. Landon BE, Normand SL, Lessler A, et al. Quality of care for the treatment of acute medical conditions in US hospitals. Arch Intern Med. 2006;166(22):2511-2517. PubMed
24. Mueller SK, Lipsitz S, Hicks LS. Impact of hospital teaching intensity on quality of care and patient outcomes. Med Care.2013;51(7):567-574. PubMed
25. Lopez L, Hicks LS, Cohen AP, McKean S, Weissman JS. Hospitalists and the quality of care in hospitals. Arch Intern Med. 2009;169(15):1389-1394. PubMed
26. Barreto-Filho JA, Wang Y, Rathore SS, et al. Transfer rates from nonprocedure hospitals after initial admission and outcomes among elderly patients with acute myocardial infarction. JAMA Intern Med. 2014;174(2):213-222. PubMed
27. Carlson JE, Zocchi KA, Bettencourt DM, et al. Measuring frailty in the hospitalized elderly: concept of functional homeostasis. Am J Phys Med Rehabil. 1998;77(3):252-257. PubMed
28. Ward E, Jemal A, Cokkinides V, et al. Cancer disparities by race/ethnicity and socioeconomic status. CA Cancer J Clin. 2004;54(2):78-93. PubMed
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