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Original Investigation |

Functional Impairment and Hospital Readmission in Medicare Seniors FREE

S. Ryan Greysen, MD, MHS, MA1; Irena Stijacic Cenzer, MA2,3; Andrew D. Auerbach, MD, MPH1; Kenneth E. Covinsky, MD, MPH2,3,4
[+] Author Affiliations
1Division of Hospital Medicine, University of California, San Francisco
2Division of Geriatric Medicine, University of California, San Francisco
3San Francisco Veterans Affairs Medical Center, San Francisco, California
4Associate Editor, JAMA Internal Medicine
JAMA Intern Med. 2015;175(4):559-565. doi:10.1001/jamainternmed.2014.7756.
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Published online

Importance  Medicare currently penalizes hospitals for high readmission rates for seniors but does not account for common age-related syndromes, such as functional impairment.

Objective  To assess the effects of functional impairment on Medicare hospital readmissions given the high prevalence of functional impairments in community-dwelling seniors.

Design, Setting, and Participants  We created a nationally representative cohort of 7854 community-dwelling seniors in the Health and Retirement Study, with 22 289 Medicare hospitalizations from January 1, 2000, through December 31, 2010.

Main Outcomes and Measures  Outcome was 30-day readmission assessed by Medicare claims. The main predictor was functional impairment determined from the Health and Retirement Study interview preceding hospitalization, stratified into the following 5 levels: no functional impairments, difficulty with 1 or more instrumental activities of daily living, difficulty with 1 or more activities of daily living (ADL), dependency (need for help) in 1 to 2 ADLs, and dependency in 3 or more ADLs. Adjustment variables included age, race/ethnicity, sex, annual income, net worth, comorbid conditions (Elixhauser score from Medicare claims), and prior admission. We performed multivariable logistic regression to adjust for clustering at the patient level to characterize the association of functional impairments and readmission.

Results  Patients had a mean (SD) age of 78.5 (7.7) years (range, 65-105 years); 58.4% were female, 84.9% were white, 89.6% reported 3 or more comorbidities, and 86.0% had 1 or more hospitalizations in the previous year. Overall, 48.3% had some level of functional impairment before admission, and 15.5% of hospitalizations were followed by readmission within 30 days. We found a progressive increase in the adjusted risk of readmission as the degree of functional impairment increased: 13.5% with no functional impairment, 14.3% with difficulty with 1 or more instrumental activities of daily living (odds ratio [OR], 1.06; 95% CI, 0.94-1.20), 14.4% with difficulty with 1 or more ADL (OR, 1.08; 95% CI, 0.96-1.21), 16.5% with dependency in 1 to 2 ADLs (OR, 1.26; 95% CI, 1.11-1.44), and 18.2% with dependency in 3 or more ADLs (OR, 1.42; 95% CI, 1.20-1.69). Subanalysis restricted to patients admitted with conditions targeted by Medicare (ie, heart failure, myocardial infarction, and pneumonia) revealed a parallel trend with larger effects for the most impaired (16.9% readmission rate for no impairment vs 25.7% for dependency in 3 or more ADLs [OR, 1.70; 95% CI, 1.04-2.78]).

Conclusions and Relevance  Functional impairment is associated with increased risk of 30-day all-cause hospital readmission in Medicare seniors, especially those admitted for heart failure, myocardial infarction, or pneumonia. Functional impairment may be an important but underaddressed factor in preventing readmissions for Medicare seniors.

Unplanned hospital readmission affects 15% to 30% of Medicare patients, with costs exceeding $17 billion annually.1 Accordingly, the Centers for Medicare & Medicaid Services (CMS) and others have called for focused efforts to reduce hospital readmission rates.24 The implementation of a controversial CMS Hospital Readmission Reduction Program (HRRP) in 2012 as a core quality improvement and cost-savings component of the Affordable Care Act underscores the importance of this issue in national health care policy.5,6 Despite intense efforts, prediction of readmission risk remains imprecise,7 and growing evidence suggests that unmeasured patient-related factors may be at the heart of variations in hospital readmission rates.8 Ironically, while more than 80% of Medicare’s 50 million beneficiaries are 65 years or older,9 the effect on readmission of common patient-level geriatric conditions, such as functional impairment, has not been extensively explored.

Functional impairment is highly prevalent in community-dwelling Medicare beneficiaries, and associations with acute care use and mortality are well known.10,11 Acute illness has profound effects on functional status in older adults; thus, impairment is even more common for hospitalized adults.12,13 Functional status has also been linked to important outcomes for hospitalized older adults, such as nursing home placement or death within 1 year14,15; however, few studies have examined the role of functional impairment on readmission specifically. Existing studies have suggested a relationship but are limited by single-site data, short duration of follow-up, or small sample size that cannot be reliably extrapolated to the entire Medicare population.1618 Functional impairment has also been hypothesized to play a key role in posthospitalization syndrome that may predispose older vulnerable adults to readmission.19 Unfortunately, previous high-quality readmission studies that rely on Medicare data have been unable to assess the effects of functional impairment because functional status of hospitalized Medicare beneficiaries is not reported to the CMS.20,21

To address these gaps in the literature, we used longitudinal, nationally representative survey data from the Health and Retirement Study (HRS), which includes functional assessments of community-dwelling Medicare beneficiaries linked to Medicare claims from January 1, 2000, through December 31, 2010. We applied criteria used in the current CMS readmission penalty and examined the effects of functional impairment on 30-day hospital readmission. We hypothesized that functional impairment would be associated with 30-day readmission and that severity of impairment would be correlated with higher odds for readmission. Greater understanding of functional vulnerability is crucial to improving transitions of care and increasing attention to often overlooked functional issues for older adults in light of the new Medicare HRRP policy.

Participants

The HRS was designed to examine changes in health and wealth as people age.22,23 The HRS is an ongoing, nationally representative longitudinal study of participants 50 years or older. Follow-up surveys are administered to all participants in waves every 2 years; response rates range from 80% to 90%, and more than 85% of participants agree to have their responses linked to their Medicare claims data. The study started in 1992, and new community-dwelling participants are recruited every 6 years to remain representative of the aging US population. If a participant is not able to complete an interview, the interview is conducted with a proxy respondent (between 6.8% and 11.2% of interviews were conducted by proxies in the 2000-2008 waves). Detailed information on steps taken by the HRS to recruit and maintain a representative sample of older community-dwelling adults is described in a series of HRS methods papers available on the HRS website.24,25 Participants provided written consent to enroll in the HRS. This study was approved by the University of California, San Francisco Institutional Review Board.

We created a cohort of community-dwelling participants 65 years or older and admitted to a hospital between January 1, 2000, and December 31, 2010. We included participants who enrolled in the HRS after 2000 provided they were 65 years or older. Similarly, patients who were already enrolled in the HRS in 2000 but were not yet 65 years were included in the cohort once they reached 65 years. To identify hospital admissions, we linked HRS survey data to Medicare claims and searched for inpatient claims in Medicare files. Of 16 719 participants with their HRS surveys linked to Medicare claims, 10 146 (60.7%) were admitted to an eligible hospital (acute care hospitals only; no rehabilitation or prospective payment systems–exempt cancer hospitals) at least once during the sampling frame, resulting in 31 289 unique admissions. Following CMS policy for the HRRP, we excluded admissions for the following reasons: transition to health maintenance organization plan within 30 days of discharge, as the CMS readmission penalty does not apply to managed care admissions (2680 [8.6%]); death in hospital or within 30 days of discharge (2400 [7.7%]); transfer to another acute care facility before discharge (1125 [3.6%]); less than 12 months of Medicare claims before hospital admission, which is required to determine comorbidities from International Classification of Diseases, Ninth Revision, codes (854 [2.7%]); and discharge against medical advice (67 [0.3%]). We also excluded participants with no HRS interview within the preceding 2 survey waves (1874 [6.0]%), resulting in a final sample of 22 289 admissions from 7854 participants.

Measures
Primary Predictor: Functional Impairment

We used 2 widely used measures of functional impairment—activities of daily living (ADL) and instrumental ADL (IADL). Both measures were obtained from the HRS interview immediately preceding hospital admission. The ADL scale comprises a series of self-care activities essential to living independently in the community2628 that include bathing, dressing, transferring (eg, moving from bed to chair), toileting, and eating. The IADL impairments require higher levels of functioning, and difficulties often signal a need for ongoing care from family members or health care professionals.29 For the IADL scale, we used taking medications as prescribed, managing finances, shopping for groceries or clothing, preparing meals, using the telephone, and using transportation within the community. For both ADL and IADL, we operationalized responses into binary variables of those reporting any ADL or IADL difficulty vs those reporting no difficulties. Difficulty in any ADL or IADL implies the task is burdensome but can be accomplished without assistance from another person. In addition, for ADL impairments, we created an ordinal 3-level variable as follows: no dependencies, 1 to 2 ADL dependencies, and 3 or more ADL dependencies. Dependency in any ADL implies the individual cannot accomplish that task without assistance from another person. We created an ordinal 5-level classification to integrate IADL and ADL difficulty and dependency as predictors of readmission as follows: no functional impairments, difficulty with 1 or more IADL, difficulty with 1 or more ADL, dependency in 1 to 2 ADLs, and dependency in 3 or more ADLs. This 5-level classification reflects the clinical continuum of functional status and typical natural history of impairment in which individuals sequentially develop IADL difficulty, then ADL difficulty, then 1 or 2 ADL dependencies, and ultimately multiple ADL dependencies.30

Main Outcome: 30-Day Readmission

We used CMS data to identify date of discharge for each index admission; those with another admission within 30 days were classified as a readmission. Overall, 15.5% of hospitalizations were followed by a 30-day readmission, representing 3457 readmissions (2343 individuals).

Other Measurements

We considered health and demographic factors shown to effect 30-day readmission in prior studies that could introduce confounding into our analyses. Demographic factors included age, sex, race and/or ethnicity, marital status, educational level, annual income, and wealth. Health factors included the Elixhauser comorbidity score calculated from International Classification of Diseases, Ninth Revision, codes and any hospitalization within 1 year before the index admission. Income was measured by asking participants to report their total household income in the previous calendar year. Net worth was measured by asking participants to report their total assets and debts. Comorbidities for the Elixhauser score and age were determined from Medicare hospital claims data at the time of index admission. All other data above were derived from the HRS survey immediately preceding hospitalization.

Statistical Analysis

We analyzed our cohort of hospitalized Medicare seniors to determine the effects of functional impairment on all-cause hospital readmission within 30 days. Given multiple admissions per HRS participant, we used admissions rather than individual participants as our unit of analysis. This analytical decision also reflects the clinical reality that many older adults face multiple admissions over time and mirrors the CMS-HRRP policy as well. We used logistic regression with robust variance estimation (ie, sandwich estimator) to adjust for clustering of admissions within individuals. Regressions do not account for the complex survey design of the HRS but do adjust for the differential probability of selection and for clustering of admissions at the patient level. This adjustment was performed by using the cluster option to the appropriately weighted logistic regression command in Stata, version 12 (StataCorp LP).

Table 1 describes distributions of each risk factor among those readmitted and not readmitted within 30 days of the index admission. We tested the difference in distribution using χ2 tests for binary and categorical variables and t test for continuous variables, accounting for differential probability of selection and the complex sampling design of the HRS. Next, using readmission within 30 days as a dichotomous variable, we examined the relationship between functional status and readmission using unadjusted and adjusted logistic regression. We used multivariable logistic regression to adjust outcomes for all demographic and health risk factors described above. We also performed a test of trend to examine whether overall increasing degrees of functional impairment across levels was associated with overall increasing risk of readmission. To determine whether longer time from functional measurement and index admission might influence results, we also performed a sensitivity analysis limited to admissions with functional measurements taken within the preceding 6 months. Since hospitals and the CMS focus on rates (rather than odds ratios [ORs]) for readmission, we also used the same adjustor variables to model predicted readmission rates (predicted probability). Finally, to maximize alignment of our analyses with the current Medicare HRRP, we performed a subanalysis restricted to hospital admissions for 3 diagnoses targeted by the HRRP: heart failure, myocardial infarction, and pneumonia.

Table Graphic Jump LocationTable 1.  Cohort Characteristics of the Health and Retirement Study–Medicare 2000-2010

As shown in Table 1, complete data were available for 22 289 hospital admissions (7854 participants). Ages ranged from 65 to 105 years (mean [SD], 78.5 [7.7]); 58.4% were female, 84.9% were white, 89.6% reported 3 or more comorbidities, and 86.0% had 1 or more hospitalization in the year preceding their index hospital admission. Overall, 15.5% of hospital admissions had a readmission within 30 days. Several patient characteristics differed significantly for admissions with a readmission within 30 days vs those without: nonwhite race/ethnicity (16.8% vs 14.8%), annual income ($21 000 vs $24 000), net wealth ($103 000 vs $137 000), less than a high school education (35.0% vs 31.6%), fair or poor self-rated health (56.4% vs 46.9%), number of Elixhauser comorbidities (7.2 vs 5.7), and 1 or more hospitalization in the year before the index admission (80.8% vs 86.9%).

Overall, 48.3% of patients had some level of functional impairment before hospital admission (Table 1). In multivariable regression analysis (Table 2), we found a progressive increase in adjusted risk of readmission as the degree of functional impairment increased (test for trend, P < .001): 13.5% with no functional impairment, 14.3% with 1 or more IADL difficulty (OR, 1.06; 95% CI, 0.94-1.20), 14.4% with 1 or more ADL difficulty (OR, 1.08; 95% CI, 0.96-1.21), 16.5% with dependency in 1 to 2 ADLs (OR, 1.26; 95% CI, 1.11-1.44), and 18.2% with dependency in 3 or more ADLs (OR, 1.42; 95% CI, 1.20-1.69). Results from a sensitivity analysis limited to admissions with functional measurements taken within the preceding 6 months were not significantly different from results from the unrestricted analysis.

Table Graphic Jump LocationTable 2.  Association of Functional Impairments With Hospital Readmission

In a subanalysis restricted to patients admitted with conditions targeted by the current Medicare HRRP (ie, heart failure, myocardial infarction, and pneumonia), 19.2% of admissions were associated with a 30-day readmission. Multivariable regression revealed a trend similar to that of the full sample with respect to rising odds of readmission with increasing impairment. Rates of readmission were higher in each category of impairment, but effect sizes were similar to those of the full sample except for the most-impaired category (Table 3): 16.9% with no functional impairment, 16.5% with 1 or more IADL difficulty (OR, 0.97; 95% CI, 0.66-1.44), 18.8% with 1 or more ADL difficulty (OR, 1.14; 95% CI, 0.82-1.58), 18.4% with dependency in 1 to 2 ADLs (OR, 1.11; 95% CI, 0.77-1.61), and 25.7% for dependency in 3 or more ADLs (OR, 1.70; 95% CI, 1.04-2.78).

Table Graphic Jump LocationTable 3.  Functional Impairment and Readmission for Targeted Medicare Diagnosesa

In this 10-year longitudinal, nationally representative study of hospital admissions among Medicare seniors, approximately half (48.3%) had functional impairments, which are associated with higher readmission rates. In addition, the risk of readmission increased in a dose-response fashion as the severity of impairment increased; patients with the most functional impairments were 42% more likely to be readmitted compared with those with no impairments. Despite the prevalence of these impairments and well-known associations with outcomes of care in this population, functional status has been overlooked in current analyses of readmission. A recent systematic review of readmission risk prediction models found that only 2 of 30 high-quality studies included functional status as a predictor or adjustment variable.7 Thus, unmeasured functional impairments may play a key mechanistic role in what has been described as posthospitalization syndrome, which is a condition of elevated generalized risk for poor health outcomes within 30 days of discharge due to patients’ inability to care for themselves, manage their affairs, and recover from their hospitalization that leads to readmission shortly after discharge.19 Our findings suggest that this condition of generalized risk may be rooted in prehospitalization functional impairments. Our findings also build on smaller or single-site studies showing a consistent relationship between functional impairment and readmission.13,1618

This association between functional impairment and readmission has important policy and financial implications for hospitals. The difference in readmission rates we demonstrate, while modest in absolute terms (10% difference between unimpaired and the most impaired), can translate to substantial penalties for individual hospitals under the new CMS readmission reduction program. In 2014, hospitals with unplanned readmission rates a few percentage points above the expected rate calculated by the CMS readmission policy faced annual reimbursement penalties up to 2% (up to 3% in 2015), which may represent most operating budget margins for many hospitals. In 2013 alone, 2225 hospitals (66% of eligible US hospitals) were penalized for excess readmissions under the new HRRP, resulting in $227 million in withheld reimbursements.31 In our subanalyses restricted to patients with index admission diagnoses targeted by the current CMS policy (ie, heart failure, myocardial infarction, and pneumonia), the effects were even larger among the most functionally impaired—those with 3 or more ADL dependencies were 70% more likely to be readmitted than those with no impairments. These subanalysis findings modeled after the current scope of the Medicare readmission policy suggest that functional impairments may already have financial implications for US hospitals even if Medicare does not expand the penalty to include hospital-wide readmissions as currently proposed.32

Beyond any possible effect from readmission penalties, functional impairments place a heavy burden on hospitalized seniors and their caregivers, thus providing additional patient-centered motivation for hospitals to identify patients with functional impairments on admission. Unlike more complex problems, such as unstable housing,33 low socioeconomic status,34 or other factors contributing to poor postdischarge environment,35 previous studies have consistently demonstrated the efficacy of well-defined interventions targeted at patients with functional impairments.36,37 Furthermore, measuring IADL and ADL impairments in hospital settings is easy. As a series of simple questions asked of patients or their caregivers, it requires no special equipment or training for staff and is often included in nursing intake assessments, although this information often is not copied to physician notes or billing documentation, thus preventing the routine use of this information in hospital- or system-wide analyses of readmission or other outcomes of hospitalization.

Indeed, lack of adequate documentation, billing, or reporting of functional impairment is likely a key barrier to the prior and current use of functional impairment in readmission risk prediction models and transition interventions. For more than a decade, Medicare has required collection and reporting of data on functional status in most postacute care settings, including skilled nursing facilities, acute rehabilitation facilities, long-term acute care hospitals, nursing homes, and home health agencies.38 While Medicare is currently developing uniform standards for functional assessment across these postacute care settings,39 acute care hospitals are still not required or incentivized to collect and report any measures of functional status in hospitalized seniors. Given Medicare’s current policy focus on reducing readmissions, the consistency of a hospital’s assessment of function on admission might be an excellent target for future quality metrics.

Our study has several limitations. First, given the prospective nature of the HRS study, the time from our measurements of functional impairment and hospitalization were not uniform among HRS participants (interquartile range, 213-622 days; mean, 430 days). Although results of our subanalyses of participants with functional assessments within 6 months preceding admission were not significantly different from our main results, our analysis may underestimate the effects of functional impairments at the actual time of hospital admission, as functional status typically declines in the setting of acute illness.15,40 Second, we do not have Medicare claims data after 2012, when the current CMS readmission penalty was enacted; however, readmission rates have been publicly reported by the CMS since 2009, and national unadjusted readmission rates have been stable at 18% to 19% from 2007 to 2012.41 Finally, although we created our cohort of hospitalized patients and our outcome of readmission to mirror the CMS readmission policy, we did not use the same adjustment procedures as the CMS to calculate readmission rates. Since the CMS intentionally does not adjust for factors such as sex, race/ethnicity, and socioeconomic status, our analyses are comparatively overadjusted and our estimated readmission rates are therefore conservative with respect to actual application of the CMS readmission policy.

We found that nearly half of hospitalized Medicare seniors have preadmission functional impairments. Increasing severity of these functional impairments is associated with increased risk of 30-day all-cause hospital readmission, especially among patients admitted for heart failure, myocardial infarction, or pneumonia. Functional impairment on admission may be an overlooked but highly suitable target for interventions to reduce Medicare hospital readmissions.

Accepted for Publication: November 13, 2014.

Corresponding Author: S. Ryan Greysen, MD, MHS, MA, Division of Hospital Medicine, University of California, San Francisco, 533 Parnassus Ave, PO Box 0131, San Francisco, CA 94113 (ryan.greysen@ucsf.edu).

Published Online: February 2, 2015. doi:10.1001/jamainternmed.2014.7756.

Author Contributions: Dr Greysen had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Greysen, Auerbach, Covinsky.

Acquisition, analysis, or interpretation of data: Greysen, Cenzer, Covinsky.

Drafting of the manuscript: Greysen, Auerbach, Covinsky.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Greysen, Cenzer, Covinsky.

Obtained funding: Greysen, Covinsky.

Administrative, technical, or material support: Covinsky.

Study supervision: Auerbach, Covinsky.

Conflict of Interest Disclosures: None reported.

Funding/Support: Dr Greysen is supported by the National Institutes of Health (NIH), National Institute of Aging (NIA) through the Claude D. Pepper Older Americans Independence Center (grant P30AG021342 NIH/NIA), a Career Development Award (grant 1K23AG045338-01), and the NIH-NIA Loan Repayment Program. Dr Covinsky is supported by the NIA through a K-24 Career Mentoring Award and an R01 from the National Institute for Nursing Research.

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: Dr Covinsky is an associate editor of JAMA Internal Medicine but was not involved in the editorial review or the decision to accept the manuscript for publication.

Previous Presentations: This article was presented as a Presidential Poster Finalist and recognized with the New Investigator Award at the 2014 Annual Meeting of the American Geriatrics Society; May 14, 2015; Lake Buena Vista, Florida; and was also presented at the 2014 Annual Meeting of the Society for Hospital Medicine; March 25, 2014; Las Vegas, Nevada.

Additional Contributions: John Boscardin, PhD, Divisions of Biostatistics and Epidemiology and Geriatric Medicine, University of California, San Francisco, provided expert statistical advice. He was not compensated for his contribution.

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Doran  KM, Ragins  KT, Iacomacci  AL, Cunningham  A, Jubanyik  KJ, Jenq  GY.  The revolving hospital door: hospital readmissions among patients who are homeless. Med Care. 2013;51(9):767-773.
PubMed   |  Link to Article
Calvillo-King  L, Arnold  D, Eubank  KJ,  et al.  Impact of social factors on risk of readmission or mortality in pneumonia and heart failure: systematic review. J Gen Intern Med. 2013;28(2):269-282.
PubMed   |  Link to Article
Arbaje  AI, Wolff  JL, Yu  Q, Powe  NR, Anderson  GF, Boult  C.  Postdischarge environmental and socioeconomic factors and the likelihood of early hospital readmission among community-dwelling Medicare beneficiaries. Gerontologist. 2008;48(4):495-504.
PubMed   |  Link to Article
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.
PubMed   |  Link to Article
Naylor  MD, Brooten  D, Campbell  R,  et al.  Comprehensive discharge planning and home follow-up of hospitalized elders: a randomized clinical trial. JAMA. 1999;281(7):613-620.
PubMed   |  Link to Article
Clauser  SB, Bierman  AS.  Significance of functional status data for payment and quality. Health Care Financ Rev. 2003;24(3):1-12.
PubMed
Summary of feedback from the Technical Expert Panel on the Development of Cross-Setting Functional Status Quality Measures.http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/LTCH-Quality-Reporting/Downloads/Summary-of-Feedback-from-TEP-on-the-Development-of-Cross-Setting-Functional-Status-Quality-.pdf. Accessed May 23, 2014.
Fortinsky  RH, Covinsky  KE, Palmer  RM, Landefeld  CS.  Effects of functional status changes before and during hospitalization on nursing home admission of older adults. J Gerontol A Biol Sci Med Sci. 1999;54(10):M521-M526.
PubMed   |  Link to Article
Gerhardt  G, Yemane  A, Hickman  P,  et al. Data shows reduction in Medicare hospital readmission rates during 2012.http://www.cms.gov/mmrr/Downloads/MMRR2013_003_02_b01.pdf. Accessed May 23, 2014.

Figures

Tables

Table Graphic Jump LocationTable 1.  Cohort Characteristics of the Health and Retirement Study–Medicare 2000-2010
Table Graphic Jump LocationTable 2.  Association of Functional Impairments With Hospital Readmission
Table Graphic Jump LocationTable 3.  Functional Impairment and Readmission for Targeted Medicare Diagnosesa

References

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Doran  KM, Ragins  KT, Iacomacci  AL, Cunningham  A, Jubanyik  KJ, Jenq  GY.  The revolving hospital door: hospital readmissions among patients who are homeless. Med Care. 2013;51(9):767-773.
PubMed   |  Link to Article
Calvillo-King  L, Arnold  D, Eubank  KJ,  et al.  Impact of social factors on risk of readmission or mortality in pneumonia and heart failure: systematic review. J Gen Intern Med. 2013;28(2):269-282.
PubMed   |  Link to Article
Arbaje  AI, Wolff  JL, Yu  Q, Powe  NR, Anderson  GF, Boult  C.  Postdischarge environmental and socioeconomic factors and the likelihood of early hospital readmission among community-dwelling Medicare beneficiaries. Gerontologist. 2008;48(4):495-504.
PubMed   |  Link to Article
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.
PubMed   |  Link to Article
Naylor  MD, Brooten  D, Campbell  R,  et al.  Comprehensive discharge planning and home follow-up of hospitalized elders: a randomized clinical trial. JAMA. 1999;281(7):613-620.
PubMed   |  Link to Article
Clauser  SB, Bierman  AS.  Significance of functional status data for payment and quality. Health Care Financ Rev. 2003;24(3):1-12.
PubMed
Summary of feedback from the Technical Expert Panel on the Development of Cross-Setting Functional Status Quality Measures.http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/LTCH-Quality-Reporting/Downloads/Summary-of-Feedback-from-TEP-on-the-Development-of-Cross-Setting-Functional-Status-Quality-.pdf. Accessed May 23, 2014.
Fortinsky  RH, Covinsky  KE, Palmer  RM, Landefeld  CS.  Effects of functional status changes before and during hospitalization on nursing home admission of older adults. J Gerontol A Biol Sci Med Sci. 1999;54(10):M521-M526.
PubMed   |  Link to Article
Gerhardt  G, Yemane  A, Hickman  P,  et al. Data shows reduction in Medicare hospital readmission rates during 2012.http://www.cms.gov/mmrr/Downloads/MMRR2013_003_02_b01.pdf. Accessed May 23, 2014.

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