Secondary Logo

Journal Logo

AAPA Members can view Full text articles for FREE. Not a Member? Join today!

Care transitions in a changing healthcare environment

Callahan, Kathryn E. MD, MS; Hartsell, Zachary MHA, PA-C

Journal of the American Academy of PAs: September 2015 - Volume 28 - Issue 9 - p 29–35
doi: 10.1097/01.JAA.0000470433.84446.c3
CME: Internal Medicine
Free
CME

ABSTRACT Readmissions are a significant element in the ongoing healthcare debate, and new evidence suggests that high readmissions can be a surrogate marker for poor quality healthcare. Additionally, although readmissions can offer a financial incentive for some hospitals, that model is being phased out; readmissions in a pay-for-performance or bundled payment model represent significant financial risk for providers and hospitals. Although no specific strategy at discharge has proven to be effective in reducing readmissions, practices that include good posthospital communication to the patient and care team, access to follow-up, and attention to mobility and self-care deficits are important factors in limiting readmissions. PAs play a key role in assessing for high readmission risk and implementing prevention strategies in real time.

Kathryn E. Callahan is an assistant professor in the Section on Gerontology and Geriatric Medicine at Wake Forest School of Medicine in Winston-Salem, N.C. Zachary Hartsell is director of the PA program at Wake Forest School of Medicine and director of PA services at Wake Forest Baptist Medical Center. The authors have disclosed no potential conflicts of interest, financial or otherwise.

Earn Category I CME Credit by reading both CME articles in this issue, reviewing the post-test, then taking the online test at http://cme.aapa.org. Successful completion is defined as a cumulative score of at least 70% correct. This material has been reviewed and is approved for 1 hour of clinical Category I (Preapproved) CME credit by the AAPA. The term of approval is for 1 year from the publication date of September 2015.

Figure

Figure

Box 1

Box 1

Discussion of hospital readmissions has typically focused on their cost implications, but high hospital readmissions are increasingly recognized as representing poor quality care.1,2 Rates of 30-day readmission for Medicare beneficiaries are near 20%, and 90-day readmissions are up to 34%.3 The Medicare Payment Advisory Committee estimates that improvements in the safety of transitions from hospital to home could prevent up to one-third of unplanned readmissions. Through provisions in the Patient Protection and Affordable Care Act (ACA), the Centers for Medicare and Medicaid Services (CMS) has mandated reduced payments for hospitals with 30-day readmission rates above the projected value for target conditions.2 Within the present fee-for-service model, readmissions may still be in a hospital's best financial interest, as penalties for readmissions are often lower than the revenue they produce.4 However, as hospitals move from fee-for-service volume-based care toward population-focused value-based care, unplanned hospital readmissions will lose their financial ambiguity and become an area in which hospitals need to control cost.

Unfortunately, solutions are complicated; research suggests that many readmissions are driven by factors beyond hospital control, such as mental illness and poverty.5 This article will discuss the value of measuring hospital readmissions as a surrogate for quality of care, make the cost-reduction case for lowering readmissions, and offer practical tools to help physician assistants (PAs) reduce hospital readmissions.

Box 2

Box 2

Back to Top | Article Outline

COSTS OF REHOSPITALIZATION

Healthcare policy experts point to readmission reduction as a means to eliminate wasteful spending.1 At the same time, many hospitals rely on the revenue generated from readmissions to maintain their operating margin. Up to 12% of Medicare patient hospitalizations are due to readmission.4 Although penalties for high readmission rates can be steep, legislation and program logistics capped the penalties at a 1% reduction in Medicare payments to hospitals in 2013, eventually rising to 3% in subsequent years.4,6 Given that readmission reduction strategies have significant short-term costs, the long-term rate of return for organizations implementing a successful readmission program ranges from 41% to 112%.6 The combination of loss of current income combined with the unclear future return on investment of implemented readmission strategies give hospitals little financial incentive to reduce readmissions. Policymakers will need to carefully balance developing short-term support for hospitals mandated to improve readmission rates while maintaining long-term incentives for organizations to willingly change their delivery models to an episodic or performance-based system.

More importantly, unplanned hospital readmissions have been posited, amidst debate, as a surrogate marker for overall poor quality of care for adults.5,7,8 Readmission negatively affects patient function and quality of life.5,7,8 About one-third of inpatients over age 70 years leave the hospital with a new impairment in their activities of daily living (ADLs) or instrumental activities of daily living (IADLs).9,10 This occurs despite patient recovery from the medical illness that led to hospitalization. Well-described “hazards of hospitalization” for older adults stem from immobility, muscle loss, bone resorption, and vasomotor instability.11 Prognosis for functional recovery is poor; at one year, 41% of older adults with a new hospitalization-related ADL deficit have died, and 28% have not regained baseline function.12 More than half of disability in older adults requiring long-term care begins at hospitalization.10,12 Recurrent hospital admissions are also associated with higher mortality.13 Thus, recurrent hospitalization carries high risk for morbidity and disability, leading to loss of independence and death—unwelcome events for previously functional older adults.

The ACA's hospital readmission reduction program reduces Medicare reimbursements for hospital systems nationwide whose readmissions for target conditions fall above the expected rate.14 Specifically, this program measures hospitals' risk-standardized all-cause 30-day readmission ratio following index hospitalizations for the targeted conditions of heart failure, acute myocardial infarction (AMI), or pneumonia. Readmission reduction is part of CMS's current strategic plan. Chronic obstructive pulmonary disease (COPD) and stroke readmissions are proposed for fiscal year 2016 (October 1, 2015, to September 30, 2016), with vascular surgery and joint replacements being considered as other potential targets.2,14 Under this system, hospitals with unfavorable readmission ratios incur payment penalties, which reduce Medicare base reimbursements for inpatient services rendered for all diagnosis-related groups.2

Back to Top | Article Outline

PATIENT-SPECIFIC RISK FACTORS

The system is clearly moving toward defining target readmission rates for all conditions. Therefore, PAs must become comfortable identifying patients at highest risk for adverse reactions during a care transition. Readmissions prediction generally is split into two major divisions: individual patient characteristics and breakdowns in systems of care. The best studied patient-specific risk factors for avoidable readmissions include admission diagnosis, comorbidity, sociodemographic factors (age, race, ethnicity, and sex), and social determinants of health (socioeconomic status, caregiver support, marital status, literacy, and health literacy).15-25

Back to Top | Article Outline

Admitting diagnosis and comorbidities

Patient medical history and comorbidities contribute to rehospitalizations. Heart failure is the most likely admitting diagnosis after discharge for any condition.3 Higher readmission rates after acute MI, pneumonia, and heart failure have led to the initial targeting of these admitting diagnoses to reduce readmissions.26 Measures of severity of illness, including acuity at admission, length of stay, clinical measures specific to the disease process, or medical complications after a surgical procedure, may each increase risk.15,17,24,27,28 Diagnoses such as diabetes and hypertension, asthma, coronary artery disease, and chronic pulmonary disease have progressive courses, complex management, and the potential for exacerbations.3,28-31 However, the most commonly cited medical risk for readmission is a patient's total comorbidity burden.15,17,24,27,32 Comorbidity often is defined by a patient's score on the Charlson Comorbidity Index, a method of estimating overall prognosis for patients who experience multiple comorbid conditions.33 The higher the score, the more likely the predicted outcome will result in mortality or higher resource use. Although the presence of specific diseases may contribute to readmission, disease severity and burden of diseases do not alone explain rehospitalization risk.

Back to Top | Article Outline

Depression and mental illness

Mental illness has long been recognized as a risk factor for nonadherence in chronic disease management, leading to recurrent rehospitalization and poorer outcomes.5,34 Depression is included in many risk prediction models but is nevertheless underrecognized as a target for inclusion in readmissions intervention trials for older adults.18,35,36 Depression increases risk of rehospitalization as much as threefold for high-risk older adults.37,38 Schizophrenia, bipolar disorder, and substance abuse each markedly increase readmission risk.39

Back to Top | Article Outline

Cognitive and physical functional impairment

Cognitive impairment at hospital admission may range from 15 to 35% of older adults, yet healthcare providers recognize fewer than 40% of cases.40,41 Older adults with dementia have high healthcare use rates, undergo multiple care transitions, and suffer higher mortality.42,43 The significance of functional impairment to prognosis and mortality is increasingly recognized, particularly with respect to walking speed, mobility, and dependence in ADLs and IADLs.44,45 Dependence in ADLs and IADLs and poor mobility correspond to a higher risk of hospital readmissions.46-48 Mobility and functional dependence clearly relate to poorer prognosis and represent a common outcome for disparate chronic progressive diseases.49,50

Cues that older adults with serious chronic disease are nearing the end of life include worsened mobility and functional status.50,51 Recurrent readmissions may serve as a marker that any patient with a serious chronic illness is nearing the end of life.13 Often, this marker goes unnoticed. Proper prognostication and effective communication during hospital stays and in ambulatory services can lead to appropriate palliative care and hospice, which improve quality of life and reduce risk of readmission for older adults at the end of life.52

Back to Top | Article Outline

Demographics and social determinants of health

Social factors such as older age, male sex, and minority status have been linked to increased 30-day readmission risk.3,20,53,54 Older adults who are unmarried or who live alone have shown higher risk for readmission; strong social support is associated with lower readmissions.19,22,23,55,56 Lower socioeconomic status has demonstrated inconsistent results.19,23,57 Low health literacy increases readmission risk, leading to its regular inclusion as a target for interventions.21,58-62 However, health literacy's link with education, minority status, socioeconomic status, and insurance status demonstrate the complexity of addressing these interrelated social variables. The most effective interventions promote self-advocacy and early social services involvement for caregivers to mitigate social vulnerabilities for older adults experiencing healthcare transitions.60

Back to Top | Article Outline

SYSTEM-LEVEL RISK FACTORS

Back to Top | Article Outline

Comprehensive medication management

Unplanned rehospitalization also is linked to health system issues.63-66 The most common system-level risk is medication errors due to ineffective medication reconciliation at the time of transition.67 Hospitalization results in an average of 3.1 medication changes for older adults, which can lead to challenges with medication adherence in the postacute period.68,69 The highest risk medications are insulin, other hypoglycemic agents, and cardiovascular agents such as warfarin and clopidogrel.36 Poor communication about dosage, timing, or use with other drugs or substances may lead to adverse reactions. Patients also struggle on discharge with access to medications, particularly high-cost medications.67,70 Comprehensive medication management at hospital admission and again at discharge reduces adverse reactions and readmissions.70,71

Back to Top | Article Outline

Communication and access

Communication errors cut across multiple phases of hospitalization. Fewer than 20% of hospitalizations result in direct communication between inpatient and outpatient providers.72 Systems errors and discontinuity across multiple silos of care are considered characteristic of low-quality care transitions.63,69,73 Discontinuity of care across inpatient and postacute realms may lead to diagnostic errors.57 For example, up to 40% of patients leave the hospital with test results still pending.57,74,75 Unless ambulatory providers know to follow up results, patients are at risk for avoidable readmission.65,69 Once the patient arrives in the provider's office, the discharge summary often is not available or if it is, it may not include core information about the hospitalization that the provider needs to assume care of the patient.72,76 In addition to these communication deficits, access to care is also a challenge for patients seeking follow-up care, whether through transportation difficulties or trouble booking a timely appointment.65,77

Back to Top | Article Outline

Geographic trends

Geographic variability in 30-day readmissions has been explored as a function of healthcare access, hospital use patterns, and disease prevalence. Geographic variation in index hospitalization rates coincide with the readmission rates, the implication being that communities with heavy use of hospital resources have a culture that leads to readmission.78 Readmission rates decrease in areas where community-based efforts to promote population health have been developed, supporting a role for preventive medicine and community partnership.79 Higher readmission rates occur in states with greater income inequality.80 Review of state-by-state variation in readmission rates reveals that regions with the highest rates of readmission also demonstrate the highest rates of obesity, diabetes, and cardiovascular disease.3,81 Thus, geographic variation in readmission patterns coincides with similar geographic variability in socioeconomics, healthcare use patterns, and patient-level comorbidity patterns. This supports the notion of readmissions being a marker of a community's overall health and habits.

Back to Top | Article Outline

RISK ASSESSMENT AND INTERVENTION

With quality metric groups and healthcare financing agencies examining readmissions, tools geared toward identifying high-risk groups and tools with interventions aimed to reduce the risk of readmissions have proliferated and have been endorsed by national health policymakers.4,35,81,82 Systematic review shows that most of these interventions have had uneven success.36 A meta-analysis of 43 studies found that no specific intervention showed significant effect, although interventions that included patient-centered discharge instructions and postdischarge phone calls showed a positive trend.36 Available tools may focus on risk prediction or on interventions, with most of the best-known tools incorporating both functions. The components of a successful intervention have also been the subject of much exploration. Coleman and Naylor were early leaders in developing risk assessment and prevention tools to reduce readmissions.83,84 Specifically, Coleman's early work focused on the Four Pillars:

  • effective use of the medical health record
  • timely follow-up
  • patient-centered education emphasizing the identification of red flags that signal impending complications
  • medication self-management.70

Coleman's Care Transitions Intervention (CTI) is a self-care tool supported by an interdisciplinary team of healthcare professionals working in collaboration with patients and caregivers.6 The CTI has been shown to reduce readmissions by up to 30% in specific populations. Both the Naylor nurse practitioner-based model and Coleman's CTI involve interdisciplinary healthcare teams, requiring significant upfront financial investment that may be difficult for community programs to emulate. However, models of population-based healthcare reimbursement structures suggest a clear positive return on investment using Coleman's methods in the future.83

Several additional risk assessment tools have had both high degrees of visibility and use, including Project BOOST, the Mayo Clinic Early Screen for Discharge Planning algorithm, the HOSPITAL screening tool developed at Brigham and Women's Hospital, and Project Red.

Back to Top | Article Outline

Project BOOST

Developed by the Society of Hospital Medicine, Project BOOST includes a risk assessment component, suggested interventions, and technical support from an expert mentor. The BOOST assessment is focused on the 8 P's: polypharmacy, prior hospitalizations, problem medications, psychological comorbidities, primary diagnosis, poor health literacy, poor patient support, and palliative care.85 The BOOST tool identifies patients with these characteristics who are more likely to be readmitted; the BOOST intervention component identifies measures to mitigate each factor, enacted by a specific interdisciplinary team member who also teaches the patient compensation strategies.85 BOOST further creates a network of resources for providers using its system by linking participating institutions with patient and provider education materials, physician mentors, and a support community of other member organizations. When used together, the assessment and intervention tools have been found to reduce readmissions by up to 15%.85 Limitations include the requirement that institutions buy both tools as a packaged product, a significant upfront cost for the medical center.85

Back to Top | Article Outline

Mayo Clinic Early Screen for Discharge Planning Algorithm

This algorithm is based on a review of more than 900 patients in the Mayo Clinic system over a 4-year period. The researchers analyzed 24 different variables that required specialized services at discharge.86 From the data, researchers found the following variables to be predictive of problems after discharge: advanced age, functional disability, living alone, and self-reported ambulation deficits.86 The Mayo team developed a predictive model to alert providers to patients at high risk for needing additional services at discharge. When combined with the Rankin Disability Score and embedded in the electronic health record, the algorithm helped Mayo Clinic reduce readmissions by 20%.87 Limitations include generalizability, as the predictive tool has only been used in one healthcare system.

Back to Top | Article Outline

HOSPITAL model

This model identifies seven factors found in patients at high risk for readmission within 30 days.88 By connecting the factors to the HOSPITAL acronym (Table 1) researchers were able to combine a reasonably easy to recall tool with fair discriminatory ability. Limitations of this model include its applicability across diverse populations (the initial research involved one health system). In addition, the tool is drawn from aggregate medical record data, and therefore does not include known patient-level risk factors such as mobility, health literacy, social support, or medication adherence.88 The other main limitation is that the HOSPITAL model is simply a stratification tool that lacks an intervention component.

TABLE 1

TABLE 1

Back to Top | Article Outline

Project RED

Project RED is sponsored through a partnership between Boston University, the Agency for Healthcare Research and Quality, and the National Heart, Lung and Blood Institute. The goal of the program is to overhaul current discharge processes and replace them with 12 evidence-based strategies to reduce ED visits and readmissions. The strategies were developed after an intensive review of discharge practices including gap analysis, failure mode effect analysis, process engineering, and root cause analysis. The resultant product has been shown to reduce readmissions by up to 30%.89 The primary limitation is the question of applicability at other sites of care.

Back to Top | Article Outline

GENERAL APPROACH

Apart from the structured available tools, individual providers can take steps in their own practices to ensure a smooth transition at discharge and help reduce readmissions. Kripalani suggests four core elements are critical to a successful discharge: communication between inpatient and outpatient provider, accurate medication reconciliation, effective patient-provider communication, and recognition and management of self-care deficits.72Table 2 summarizes sensible approaches for providers to take to lower the chance of readmission. Other successful strategies include integrating an interprofessional team of hospital and community resources such as case management, physical and occupational therapists, home healthcare agencies, and long-term care facilities; incorporating “teach-back” methods into standard patient education; and appropriately using hospice and palliative care resources in the community. Together, these may foster a truly patient-centered approach to help reduce hospital readmissions.

TABLE 2

TABLE 2

Back to Top | Article Outline

CONCLUSION

Hospital readmissions are a significant financial cost to the healthcare system, and a surrogate marker for poor quality care. Hospital readmissions result from many different patient-specific risks and system-level risks, such as medication errors and poor patient access to follow-up. Care transition processes that are patient-centered and team-coordinated are most effective in preventing and reducing hospital readmissions.

Back to Top | Article Outline

REFERENCES

1. Williams MV. A requirement to reduce readmissions: take care of the patient, not just the disease. JAMA. 2013;309(4):394–396.
2. Kocher RP, Adashi EY. Hospital readmissions and the Affordable Care Act: paying for coordinated quality care. JAMA. 2011;306(16):1794–1795.
3. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):1418–1428.
4. Berenson RA, Paulus RA, Kalman NS. Medicare's readmissions-reduction program—a positive alternative. N Engl J Med. 2012;366(15):1364–1366.
5. Joynt KE, Jha AK. Thirty-day readmissions—truth and consequences. N Engl J Med. 2012;366(15):1366–1369.
6. Chollet DA, Barrett A, Lake T. Readmissions in New York State: A Simulation Analysis of Alternative Payment Incentives. Princeton, NJ: Mathematic Policy Research; 2011.
7. Kangovi S, Grande D. Hospital readmissions—not just a measure of quality. JAMA. 2011;306(16):1796–1797.
8. Stefan MS, Pekow PS, Nsa W, et al. Hospital performance measures and 30-day readmission rates. J Gen Intern Med. 2013;28(3):377–385.
9. Sager MA, Franke T, Inouye SK, et al. Functional outcomes of acute medical illness and hospitalization in older persons. Arch Intern Med. 1996;156(6):645–652.
10. Covinsky KE, Pierluissi E, Johnston CB. Hospitalization-associated disability: “She was probably able to ambulate, but I'm not sure”. JAMA. 2011;306(16):1782–1793.
11. Creditor MC. Hazards of hospitalization of the elderly. Ann Intern Med. 1993;118(3):219–223.
12. Boyd CM, Landefeld CS, Counsell SR, et al. Recovery of activities of daily living in older adults after hospitalization for acute medical illness. J Am Geriatr Soc. 2008;56(12):2171–2179.
13. Lum HD, Studenski SA, Degenholtz HB, Hardy SE. Early hospital readmission is a predictor of one-year mortality in community-dwelling older Medicare beneficiaries. J Gen Intern Med. 2012;27(11):1467–1474.
14. Centers for Medicare and Medicaid Services. Readmissions Reduction Program. 2014. www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program.html. Accessed May 22, 2015.
15. Au AG, McAlister FA, Bakal JA, et al. Predicting the risk of unplanned readmission or death within 30 days of discharge after a heart failure hospitalization. Am Heart J. 2012;164(3):365–372.
16. Dharmarajan K, Hsieh AF, Lin Z, et al. Diagnoses and timing of 30-day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia. JAMA. 2013;309(4):355–363.
17. Capelastegui A, España Yandiola PP, Quintana JM, et al. Predictors of short-term rehospitalization following discharge of patients hospitalized with community-acquired pneumonia. Chest. 2009;136(4):1079–1085.
18. Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688–1698.
19. Shulan M, Gao K, Moore CD. Predicting 30-day all-cause hospital readmissions. Health Care Manag Sci. 2013;16(2):167–175.
20. Howie-Esquivel J, Dracup K. Effect of gender, ethnicity, pulmonary disease, and symptom stability on rehospitalization in patients with heart failure. Am J Cardiol. 2007;100(7):1139–1144.
21. Baker DW, Parker RM, Williams MV, Clark WS. Health literacy and the risk of hospital admission. J Gen Intern Med. 1998;13(12):791–798.
22. Arbaje AI, Wolff JL, Yu Q, et al. Postdischarge environmental and socioeconomic factors and the likelihood of early hospital readmission among community-dwelling Medicare beneficiaries. Gerontologist. 2008;48(4):495–504.
23. Moore CD, Gao K, Shulan M. Racial, income, and marital status disparities in hospital readmissions within a veterans-integrated health care network. Eval Health Prof. [e-pub June 27, 2013]
24. Muzzarelli S, Leibundgut G, Maeder MT, et al. Predictors of early readmission or death in elderly patients with heart failure. Am Heart J. 2010;160(2):308–314.
25. McCauley KM, Bixby MB, Naylor MD. Advanced practice nurse strategies to improve outcomes and reduce cost in elders with heart failure. Dis Manag. 2006;9(5):302–310.
26. Krumholz HM, Lin Z, Keenan PS, et al. Relationship between hospital readmission and mortality rates for patients hospitalized with acute myocardial infarction, heart failure, or pneumonia. JAMA. 2013;309(6):587–593.
27. Zekry D, Loures Valle BH, Graf C, et al. Prospective comparison of 6 comorbidity indices as predictors of 1-year post-hospital discharge institutionalization, readmission, and mortality in elderly individuals. J Am Med Dir Assoc. 2012;13(3):272–278.
28. French DD, Bass E, Bradham DD, et al. Rehospitalization after hip fracture: predictors and prognosis from a national veterans study. J Am Geriatr Soc. 2008;56(4):705–710.
29. McGhan R, Radcliff T, Fish R, et al. Predictors of rehospitalization and death after a severe exacerbation of COPD. Chest. 2007;132(6):1748–1755.
30. Wallace E, Hinchey T, Dimitrov BD, et al. A systematic review of the probability of repeated admission score in community-dwelling adults. J Am Geriatr Soc. 2013;61(3):357–364.
31. Allaudeen N, Schnipper JL, Orav EJ, et al. Inability of providers to predict unplanned readmissions. J Gen Intern Med. 2011;26(7):771–776.
32. Mudge AM, Kasper K, Clair A, et al. Recurrent readmissions in medical patients: a prospective study. J Hosp Med. 2011;6(2):61–67.
33. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–383.
34. Ng TP, Niti M, Tan WC, et al. Depressive symptoms and chronic obstructive pulmonary disease: effect on mortality, hospital readmission, symptom burden, functional status, and quality of life. Arch Intern Med. 2007;167(1):60–67.
35. Piraino E, Heckman G, Glenny C, Stolee P. Transitional care programs: who is left behind? A systematic review. Int J Integr Care. 2012;12:e132.
36. Hansen LO, Young RS, Hinami K, et al. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520–528.
37. Kartha A, Anthony D, Manasseh CS, et al. Depression is a risk factor for rehospitalization in medical inpatients. Prim Care Companion J Clin Psychiatry. 2007;9(4):256–262.
38. Büla CJ, Wietlisbach V, Burnand B, Yersin B. Depressive symptoms as a predictor of 6-month outcomes and services utilization in elderly medical inpatients. Arch Intern Med. 2001;161(21):2609–2615.
39. Prince JD, Akincigil A, Kalay E, et al. Psychiatric rehospitalization among elderly persons in the United States. Psychiatr Serv. 2008;59(9):1038–1045.
40. Joray S, Wietlisbach V, Büla CJ. Cognitive impairment in elderly medical inpatients: detection and associated six-month outcomes. Am J Geriatr Psychiatry. 2004;12(6):639–647.
41. Dodson JA, Truong TT, Towle VR, et al. Cognitive impairment in older adults with heart failure: prevalence, documentation, and impact on outcomes. Am J Med. 2013;126(2):120–126.
42. Callahan CM, Arling G, Tu W, et al. Transitions in care for older adults with and without dementia. J Am Geriatr Soc. 2012;60(5):813–820.
43. Feil D, Marmon T, Unützer J. Cognitive impairment, chronic medical illness, and risk of mortality in an elderly cohort. Am J Geriatr Psychiatry. 2003;11(5):551–560.
44. Studenski S, Perera S, Wallace D, et al. Physical performance measures in the clinical setting. J Am Geriatr Soc. 2003;51(3):314–322.
45. Studenski S, Perera S, Patel K, et al. Gait speed and survival in older adults. JAMA. 2011;305(1):50–58.
46. Schwarz KA, Elman CS. Identification of factors predictive of hospital readmissions for patients with heart failure. Heart Lung. 2003;32(2):88–99.
47. Campbell SE, Seymour DG, Primrose WR. A systematic literature review of factors affecting outcome in older medical patients admitted to hospital. Age Ageing. 2004;33(2):110–115.
48. Fisher SR, Kuo YF, Sharma G, et al. Mobility after hospital discharge as a marker for 30-day readmission. J Gerontol A Biol Sci Med Sci. 2013;68(7):805–810.
49. Yourman LC, Lee SJ, Schonberg MA, et al. Prognostic indices for older adults: a systematic review. JAMA. 2012;307(2):182–192.
50. Fried LP, Kronmal RA, Newman AB, et al. Risk factors for 5-year mortality in older adults: the Cardiovascular Health Study. JAMA. 1998;279(8):585–592.
51. Fried LP, Ferrucci L, Darer J, et al. Untangling the concepts of disability, frailty, and comorbidity: implications for improved targeting and care. J Gerontol A Biol Sci Med Sci. 2004;59(3):255–263.
52. Enguidanos S, Vesper E, Lorenz K. 30-day readmissions among seriously ill older adults. J Palliat Med. 2012;15(12):1356–1361.
53. Jiang HJ, Andrews R, Stryer D, Friedman B. Racial/ethnic disparities in potentially preventable readmissions: the case of diabetes. Am J Public Health. 2005;95(9):1561–1567.
54. Joynt KE, Orav EJ, Jha AK. Thirty-day readmission rates for Medicare beneficiaries by race and site of care. JAMA. 2011;305(7):675–681.
55. Rodríguez-Artalejo F, Guallar-Castillón P, Herrera MC, et al. Social network as a predictor of hospital readmission and mortality among older patients with heart failure. J Card Fail. 2006;12(8):621–627.
56. Shipton S. Risk factors associated with multiple hospital readmissions. Home Care Provid. 1996;1(2):83–85.
57. Moore C, Wisnivesky J, Williams S, McGinn T. Medical errors related to discontinuity of care from an inpatient to an outpatient setting. J Gen Intern Med. 2003;18(8):646–651.
58. McNaughton CD, Collins SP, Kripalani S, et al. Low numeracy is associated with increased odds of 30-day emergency department or hospital recidivism for patients with acute heart failure. Circ Heart Fail. 2013;6(1):40–46.
59. Mitchell SE, Sadikova E, Jack BW, Paasche-Orlow MK. Health literacy and 30-day postdischarge hospital utilization. J Health Commun. 2012;17(suppl 3):325–338.
60. Coleman EA, Smith JD, Frank JC, et al. Preparing patients and caregivers to participate in care delivered across settings: the Care Transitions Intervention. J Am Geriatr Soc. 2004;52(11):1817–1825.
61. Johnson A, Sandford J, Tyndall J. Written and verbal information versus verbal information only for patients being discharged from acute hospital settings to home. Cochrane Database Syst Rev. 2003;(4):CD003716.
62. Kripalani S, Jacobson TA, Mugalla IC, et al. Health literacy and the quality of physician-patient communication during hospitalization. J Hosp Med. 2010;5(5):269–275.
63. Coleman EA, Boult C. Improving the quality of transitional care for persons with complex care needs. J Am Geriatr Soc. 2003;51(4):556–557.
64. Halasyamani L, Kripalani S, Coleman E, et al. Transition of care for hospitalized elderly patients—development of a discharge checklist for hospitalists. J Hosp Med. 2006;1(6):354–360.
65. Misky GJ, Wald HL, Coleman EA. Post-hospitalization transitions: Examining the effects of timing of primary care provider follow-up. J Hosp Med. 2010;5(7):392–397.
66. Graham LE, Leff B, Arbaje AI. Risk of hospital readmission for older adults discharged on Friday. J Am Geriatr Soc. 2013;61(2):300–301.
67. Forster AJ, Murff HJ, Peterson JF, et al. The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161–167.
68. Boockvar K, Fishman E, Kyriacou CK, et al. Adverse events due to discontinuations in drug use and dose changes in patients transferred between acute and long-term care facilities. Arch Intern Med. 2004;164(5):545–550.
69. Cumbler E, Carter J, Kutner J. Failure at the transition of care: challenges in the discharge of the vulnerable elderly patient. J Hosp Med. 2008;3(4):349–352.
70. Coleman EA, Smith JD, Raha D, Min SJ. Posthospital medication discrepancies: prevalence and contributing factors. Arch Intern Med. 2005;165(16):1842–1847.
71. Steeb D, Webster L. Improving care transitions: optimizing medication reconciliation. J Am Pharm Assoc (2003). 2012;52(4):e43–e52.
72. Kripalani S, Jackson AT, Schnipper JL, Coleman EA. Promoting effective transitions of care at hospital discharge: a review of key issues for hospitalists. J Hosp Med. 2007;2(5):314–323.
73. Coleman EA, Berenson RA. Lost in transition: challenges and opportunities for improving the quality of transitional care. Ann Intern Med. 2004;141(7):533–536.
74. Roy CL, Rothschild JM, Dighe AS, et al. An initiative to improve the management of clinically significant test results in a large health care network. Jt Comm J Qual Patient Saf. 2013;39(11):517–527.
75. Moore C, McGinn T, Halm E. Tying up loose ends: discharging patients with unresolved medical issues. Arch Intern Med. 2007;167(12):1305–1311.
76. Horwitz LI, Jenq GY, Brewster UC, et al. Comprehensive quality of discharge summaries at an academic medical center. J Hosp Med. 2013;8(8):436–443.
77. Oduyebo I, Lehmann CU, Pollack CE, et al. Association of self-reported hospital discharge handoffs with 30-day readmissions. JAMA Intern Med. 2013;173(8):624–629.
78. Epstein AM, Jha AK, Orav EJ. The relationship between hospital admission rates and rehospitalizations. N Engl J Med. 2011;365(24):2287–2295.
79. Brock J, Mitchell J, Irby K, et al. Association between quality improvement for care transitions in communities and rehospitalizations among Medicare beneficiaries. JAMA. 2013;309(4):381–391.
80. Lindenauer PK, Lagu T, Rothberg MB, et al. Income inequality and 30 day outcomes after acute myocardial infarction, heart failure, and pneumonia: retrospective cohort study. BMJ. 2013;346:f521.
81. Centers for Disease Control and Prevention. Maps and statistics. www.cdc.gov/heartdisease/maps_statistics.htm. Accessed June 3, 2015.
82. Coleman EA, Parry C, Chalmers S, Min SJ. The care transitions intervention: results of a randomized controlled trial. Arch Intern Med. 2006;166(17):1822–1828.
83. Coleman EA, Parry C, Chalmers SA, et al. The central role of performance measurement in improving the quality of transitional care. Home Health Care Serv Q. 2007;26(4):93–104.
84. Naylor MD. Transitional care for older adults: a cost-effective model. LDI Issue Brief. 2004;9(6):1–4.
85. Quartarolo J. Project Boost: Improving Hospital Care Transitions. Society of Hospital Medicine webinar; 2014.
86. Holland DE, Harris MR, Leibson CL, et al. Development and validation of a screen for specialized discharge planning services. Nurs Res. 2006;55(1):62–71.
87. Takahashi PY, Pecina JL, Upatising B, et al. A randomized controlled trial of telemonitoring in older adults with multiple health issues to prevent hospitalizations and emergency department visits. Arch Intern Med. 2012;172(10):773–779.
88. Donzé J, Aujesky D, Williams D, Schnipper JL. Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med. 2013;173(8):632–638.
89. Boston University Medical Center. Project Red: re-engineered discharge. https://www.bu.edu/fammed/projectred. Accessed May 22, 2015.
Keywords:

readmission risk; healthcare quality; pay-for-performance; communication; follow-up; care transitions

Copyright © 2015 American Academy of Physician Assistants