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The American Journal of Managed Care September 2017
Guideline Concordance of New Statin Prescriptions: Who Got a Statin?
Thomas Cascino, MD; Marzieh Vali, MS, BS; Rita Redberg, MD, MSc; Dawn M. Bravata, MD; John Boscardin, PhD; Elnaz Eilkhani, MPH; and Salomeh Keyhani, MD, MPH
Provider-Owned Insurers
David H. Howard, PhD, and Erin Trish, PhD
The Effect of Narrow Network Plans on Out-of-Pocket Cost
Emily Meredith Gillen, PhD; Kristen Hassmiller Lich, PhD; Laurel Clayton Trantham, PhD; Morris Weinberger, PhD; Pam Silberman, JD, DrPh; and Mark Holmes, PhD
In-Gap Discounts in Medicare Part D and Specialty Drug Use
Jeah Jung, PhD; Wendy Yi Xu, PhD; and Chelim Cheong, PhD
Racial and Ethnic Differences in Hip Fracture Outcomes in Men
Lucy H. Liu, MD, MPH; Malini Chandra, MS, MBA; Joel R. Gonzalez, MPH, MPP; and Joan C. Lo, MD
Integrating Behavioral Health Under an ACO Global Budget: Barriers and Progress in Oregon
Jason Kroening-Roché, MD, MPH; Jennifer D. Hall, MPH; David C. Cameron, BA; Ruth Rowland, MA; and Deborah J. Cohen, PhD
Evaluation of a Packaging Approach to Improve Cholesterol Medication Adherence
Hayden B. Bosworth, PhD; Jamie N. Brown, PharmD, BCPS; Susanne Danus, BS; Linda L. Sanders, MPH; Felicia McCant, MSSW; Leah L. Zullig, PhD; and Maren K. Olsen, PhD
Treatment Barriers Among Younger and Older Socioeconomically Disadvantaged Smokers
Patrick J. Hammett, MA; Steven S. Fu, MD, MSCE; Diana J. Burgess, PhD; David Nelson, PhD; Barbara Clothier, MS, MA; Jessie E. Saul, PhD; John A. Nyman, PhD; Rachel Widome, PhD, MHS; and Anne M. Joseph, MD, MPH
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Against the Current: Back-Transfer as a Mechanism for Rural Regionalization
Leah F. Nelson, MD, MS; Karisa K. Harland, PhD, MPH; Dan M. Shane, PhD; Azeemuddin Ahmed, MD, MBA; and Nicholas M. Mohr, MD, MS

Against the Current: Back-Transfer as a Mechanism for Rural Regionalization

Leah F. Nelson, MD, MS; Karisa K. Harland, PhD, MPH; Dan M. Shane, PhD; Azeemuddin Ahmed, MD, MBA; and Nicholas M. Mohr, MD, MS
The authors investigated back-transfer: the transfer of patients near the end of an acute hospitalization to a local community hospital for completion of their medical care.
Multivariable Modeling
Multivariable modeling revealed several strong predictors of back-transfer (Table 4). The strongest associations were with cardiac catheterization (odds ratio [OR], 2.90; 95% CI, 1.88-4.49), having Medicaid or other state/federal-issued insurance (OR, 1.61; 95% CI, 1.11-2.33), male gender (OR, 1.57; 95% CI, 1.23-2.00), aged 51 to 65 years (OR, 1.53; 95% CI, 1.08-2.18), or older than 80 years (OR, 1.67; 95% CI 1.00-2.77). A weak association was also observed with hospital volume (OR, 0.995; 95% CI, 0.994-0.997). Several of the primary diagnoses were significant predictors of back-transfer. These were compared using the most common primary diagnosis code (CCS 7, cardiovascular disease) as a referent group. Significant disease associations with back-transfer were identified for CCS 4 (blood and blood-forming organ diseases: OR, 2.48; 95% CI, 1.04-5.91), CCS 6 (nervous system and sense organ diseases: OR, 1.95; 95% CI, 1.05-3.65), CCS 9 (digestive system diseases: OR, 1.91; 95% CI, 1.22-2.98), and CCS 13 (musculoskeletal system and connective tissue diseases: OR, 0.41; 95% CI, 0.20-0.85). Rural residence, white race, admission year, Medicare, and age between 66 and 80 years were neither positively or negatively associated with back-transfer in the multivariable model.
 
DISCUSSION
Back-transfer is a healthcare regionalization technique not previously reported for US adults. This study reports back-transfer using a large statewide claims database. The current study showed differences between back-transferred patients and those who completed their inpatient stay at one of the 5 large hospitals included in the study. Back-transferred patients were more likely than their non–back-transferred counterparts to be male, older, and white; to live in large rural areas; and to have public insurance. Total LOS was also longer for back-transferred patients. Medically, back-transferred patients had chronic diseases with multiple comorbidities and were more likely to have a cardiac catheterization. These differences appear to indicate that back-transferred patients are more medically complex.
 
It is possible that some of these findings are interconnected in that rural residents tend to be older and on public insurance,19 but not all of these differences can be attributed to the rural-urban dichotomy. Nationwide analyses from the National Center for Health Statistics has shown no significant difference between patients admitted to rural and urban hospitals for LOS or number of diagnoses.19 Further investigation is warranted to better understand the reason for these demographic and medical differences. We hypothesize that back-transferred patients may request a transfer closer to home because they have a good understanding of their chronic health conditions and anticipate a longer LOS. Furthermore, hospitals in larger rural areas may be more willing to accept back-transfers than hospitals in smaller rural areas because of greater service availability, and providers may select these hospitals for patients who have common chronic conditions.
 
Obviously, not all patients will be candidates for back-transfer. High-risk obstetric patients, those with rare diseases, or those in whom a fluctuating clinical course can be anticipated, to name a few examples, will require ongoing specialist care throughout an acute hospitalization. However, for many patients suffering from more routine diseases with a more predictable clinical course (eg, following cardiac catheterization), back-transfer may be an appropriate option.
 
Despite the rarity with which it is employed, back-transfer is a promising strategy that could better utilize health resources, so why is it so rare? We have identified several systematic barriers to widespread adoption of back-transfer in the United States. These barriers can be separated into 4 categories: policy, finance, logistics, and patient care concerns.
 
Policy barriers. No law directly forbids back-transfer of patients as long as the transfer meets the legal requirements set forth by CMS in the Emergency Medical Treatment and Active Labor Act.20 Furthermore, the “Stark Laws” apply to transfers in that large hospitals cannot incentivize patients to transfer, encourage back-transfer to specific hospitals, nor enter into contracts with small hospitals to take back-transfers (although this may change with accountable care organizations’ [ACOs’] structured hospital networks) (personal communication with Joseph Clamon, JD, January 25, 2016).21
 
Financial barriers. The current payment system for hospitals de-incentivizes back-transfer in both direct and indirect ways. Directly, hospitals that are paid on a diagnosis-related group (DRG) model stand to lose revenue by the “early” transfer of patients prior to the full DRG payment period (personal communication with A. Showers, BBA, January 8, 2016). Depending on the payment structure of the accepting hospital (Inpatient Prospective Payment System versus cost-based reimbursement at a CAH), the reimbursement from a back-transferred patient for a short acute stay may not cover the cost of caring for the patient.23 Indirectly, the sending hospital risks financial losses through disruption of referrals and early readmission. Although many tertiary care hospitals have transfer agreements that require sending hospitals to “take back” their transfers after stabilization, these clauses are rarely used. Using this mechanism to force smaller hospitals into taking a back-transfer may result in fewer transfers from that facility in the future, thus reducing revenue for the tertiary hospital in the long term (personal communication with Joseph Clamon, JD, January 25, 2016). Finally, if a patient is back-transferred, but their health deteriorates at the smaller hospital, they may be returned for further care. This would count as a readmission and likely not be reimbursed, whereas if the patient were kept at the large hospital and experienced complications, the entire care episode would be covered under the initial admission.24 Overall, current payment models do not support back-transfer and risk of financial loss likely plays a significant role in the low frequency of back-transfer.
 
Logistical barriers. The process of transferring a patient is complex: identification of an accepting hospital and physician, organizing transport, and sharing medical records all require effort. Therefore, an elective back-transfer often comes up against significant inertia within the healthcare system. It can be difficult to find a willing hospital and physician, a patient may only want to go to certain hospitals,2 differences in electronic health record systems complicate sharing of health records,25 and most back-transferred patients require ambulance transfer, which is often not covered by insurance (personal communication with Peggy O’Neill, RN, MSN, ACM, January 21, 2016). Each of these obstacles is a potential place where the transfer could derail, and failures result in wasted time for healthcare providers. Thus, the simplest solution is often to keep the patient until they are ready for discharge; consequently, the tertiary hospital’s ability to provide specialty care is diluted by provision of routine care for stable inpatients.
 
Patient care concerns. Both physicians and patients may have concerns about the ability of a small hospital to care for back-transferred patients. Providers at tertiary care hospitals often feel uncomfortable sending their patients back for continued acute management. They do not know if the patient will receive appropriate care (ie, they may not know the capabilities of the small hospital, especially CAHs). Also, the receiving hospital or physician may feel uncomfortable taking a complex patient. Furthermore, loss of continuity and treatment by multiple providers is known to worsen outcomes for patients with multiple chronic conditions or cancer, and those who have received a surgical intervention.26-28 Finally, patients themselves may have concerns regarding the quality of care at smaller local hospitals. In some cases, patients may have sought care electively at tertiary centers because they perceive higher quality and more comprehensive care.2 These patients may not want to be back-transferred to their local hospital.
 
The barriers above create challenges to increased adoption of back-transfer. However, application of this strategy may optimize use of healthcare resources, especially in rural America. Back-transfer could be nurtured as a regionalization tool through creation of a tiered system in which all hospitals are included on a registry of their capabilities,21 similar to the National Trauma Registry System. This would help providers identify appropriate back-transfer destinations and alleviate concerns about patient care. Furthermore, as healthcare reform occurs in the United States, managed care through ACOs may help network hospitals join functional “hub-and-spoke” systems, with the tertiary care center serving as the “hub” point for specialty care and patient care plan development, and smaller hospitals serving as “spokes,” managing routine management and delivering specialist-directed care. ACOs with this organization could create stable transfer agreements among several institutions based on objective criteria for transfer, or for management of specific conditions, thus avoiding problems with patient selection bias.21 Within an ACO or managed care system, shared electronic medical records could further facilitate information transfer. These organizational changes would reduce the workload associated with arranging a transfer. Clearly, these types of systemic changes will be difficult to execute without state and federal support.
 
Limitations
The results of this study must be considered in light of several limitations. First, back-transfer was a very rare event, and with our strict selection criteria, the final studied population was small. This limits the ability to make comparisons between the relatively large control group and small number of cases, and to measure variability from year to year. Second, the use of ICD-9-CM codes does not allow evaluation of disease severity or other circumstances that may have influenced providers’ decision to back-transfer patients. Third, travel distances were computed based on zip code centroids because specific address information was not available in the data. Although this method is imprecise, approximating distance is still important to appreciate a view of healthcare access for rural residents. Fourth, this dataset does not give insight into why patients were admitted or the situation of discharge from the 5 large hospitals included in this study (eg, transfer for higher level of care vs patient preference to bypass local hospitals). Fifth, because we are looking at patient-level predictors, we have not examined business relationships among hospitals or the nonprofit versus for-profit status of the hospitals in this analysis. Finally, this study used claims data from a single Midwestern state and may not be generalizable to the United States as a whole. Future work should make an effort to control for these limitations.


 
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