For-profit hospital status and rehospitalizations at different hospitals: an analysis of Medicare data

Emergency general surgery in older adult patients: Factors associated with fragmented care

BACKGROUND

Care fragmentation has been shown to lead to increased morbidity and mortality. We aimed to explore the factors related to care fragmentation after hospital discharge in geriatric emergency general surgery patients, as well as examine the association between care fragmentation and mortality.

METHODS

We designed a retrospective study of the Nationwide Readmissions Database 2019. We included patients ≥65 years old admitted with an emergency general surgery diagnosis who were discharged alive from the index admission. The primary outcome was 90-day care fragmentation, defined as an unplanned readmission to a non-index hospital. Multivariable logistic regression was performed, adjusting for patient and hospital characteristics.

RESULTS

A total of 447,027 older adult emergency general surgery patients were included; the main diagnostic category was colorectal (22.6%), and 78.2% of patients underwent non-operative management during the index hospitalization. By 90 days post-discharge, 189,622 (24.3%) patients had an unplanned readmission. Of those readmitted, 20.8% had care fragmentation. The median age of patients with care fragmentation was 76 years, and 53.2% were of female sex. Predictors of care fragmentation were living in rural counties (odds ratio 1.76, 95% confidence interval: 1.57-1.97), living in a low-income ZIP Code, discharge to intermediate care facility (odds ratio 1.28, 95% confidence interval: 1.22-1.33), initial non-operative management (odds ratio 1.17, 95% confidence interval: 1.12-1.23), leaving against medical advice (odds ratio 2.60, 95% confidence interval: 2.29-2.96), and discharge from private investor-owned hospitals (odds ratio 1.18, 95% confidence interval: 1.10-1.27). Care fragmentation was significantly associated with higher mortality.

CONCLUSION

The burden of unplanned readmissions in older adult patients who survive an emergency general surgery admission is underestimated, and these patients frequently experience care fragmentation. Future directions should prioritize evaluating the impact of initiatives aimed at alleviating the incidence and complications of care fragmentation in geriatric emergency general surgery patients.

Care fragmentation is associated with increased mortality after ileostomy creation

BACKGROUND

Ileostomy is the mainstay treatment option for various gastrointestinal conditions. Given the increased risk of post-discharge complications and high readmission rates that can be further aggravated by receiving care at different facilities (care fragmentation), further examination is necessary. We thus used a national cohort to explore the associations of care fragmentation among ileostomy patients experiencing adverse outcomes and increased hospitalization expenditures.

METHODS

All adult hospitalizations for ileostomy were tabulated from the 2016 to 2020 Nationwide Readmissions Database. Those readmitted within 90 days after discharge were included for analysis. Patients treated at a different facility than the original location where the index ileostomy was performed were categorized into the care-fragmented cohort. Multivariable regressions were developed to characterize the association of the care-fragmented cohort with postoperative outcomes, readmissions, and expenditures.

RESULTS

Of 52,254 patients with ileostomy creation hospitalizations with 90-day nonelective readmission, 9,045 (17.3%) experienced care fragmentation. Following risk adjustment, those experiencing care fragmentation faced increased odds of mortality (adjusted odds ratio 1.81, 95% confidence interval 1.54-2.12), cardiac (adjusted odds ratio 1.63, 95% confidence interval 1.42-1.85), respiratory (adjusted odds ratio 1.71, 95% confidence interval 1.53-1.91), infectious (adjusted odds ratio 1.33, 95% confidence interval 1.23-1.43), and thromboembolic (adjusted odds ratio 1.28, 95% confidence interval 1.13-1.45) complications. Furthermore, patients experiencing care fragmentation were more likely to have increased hospitalization costs ($1,700, 95% confidence interval 0.8-2.5).

CONCLUSION

Care fragmentation in ileostomy patients demonstrated an increased risk for mortality, postoperative complications, and increased hospitalization expenses. To mitigate risks for adverse outcomes, future studies should evaluate the impacts of inter-hospital communication with the goal of improving care continuity and optimizing healthcare delivery for care-fragmented populations.

Care Fragmentation Following Hospitalization for Atrial Fibrillation in the United States

Background

Despite the high prevalence of atrial fibrillation (AF), the incidence and impact of care fragmentation (CF) following hospitalization for this condition remain unstudied.

Objectives

The present study used a national database to determine the incidence of and risk factors associated with CF. Outcomes following CF were also examined.

Methods

All adults who were discharged alive following hospitalization for AF (index facility) were identified within the 2016 to 2019 Nationwide Readmissions Database. Patients requiring nonelective rehospitalization within 30 days of discharge were categorized into 2 groups. The CF cohort included those readmitted to a nonindex facility, while others were classified as noncare fragmentation. Multivariable regression was used to evaluate factors associated with CF, as well as its impact on in-hospital mortality, length of stay, and costs at rehospitalization.

Results

Of an estimated 686,942 patients who met study criteria and survived to discharge, 13.6% (n = 93,376) experienced unplanned readmission within 30 days. Among those readmitted, 21.3% (n = 19,906) were readmitted to a nonindex facility. Patients who experienced CF were younger, more commonly male and less frequently readmitted for AF. Upon multivariable adjustment, male sex, Medicaid insurance (ref: private), and transfer status were associated with increased odds of CF. Upon readmission, CF was associated with a 18% increment in relative odds of in-hospital mortality, a 0.3-day increment in length of stay, and an additional $1,500 in hospitalization costs.

Conclusions

CF was associated with significant clinical and financial burden. Further studies are needed to address factors which contribute to increased mortality and resource use following CF.

The hidden burden of unplanned readmission after emergency general surgery

BACKGROUND

There are no national studies of nonelective readmissions after emergency general surgery (EGS) diagnoses that track nonindex hospital readmission. We sought to determine the rate of overall and nonindex hospital readmissions at 30 and 90 days after discharge for EGS diagnoses, hypothesizing a significant portion would be to nonindex hospitals.

METHODS

The 2013 to 2014 Nationwide Readmissions Database was queried for all patients 16 years or older admitted with an EGS primary diagnosis and survived index hospitalization. Multivariable logistic regression identified risk factors for nonelective 30- and 90-day readmission to index and nonindex hospitals.

RESULTS

Of 4,171,983 patients, 13% experienced unplanned readmission at 30 days. Of these, 21% were admitted to a nonindex hospital. By 90 days, 22% experienced an unplanned readmission, of which 23% were to a nonindex hospital. The most common reason for readmission was infection. Publicly insured or uninsured patients accounted for 67% of admissions and 77% of readmissions. Readmission predictors at 30 days included leaving against medical advice (odds ratio [OR], 2.51 [2.47-2.56]), increased length of stay (4-7 days: OR, 1.42 [1.41-1.43]; >7 days: OR, 2.04 [2.02-2.06]), Charlson Comorbidity Index ≥2 (OR, 1.72 [1.71-1.73]), public insurance (Medicare: OR, 1.45 [1.44-1.46]; Medicaid: OR, 1.38 [1.37-1.40]), EGS patients who fell into the "Other" surgical category (OR, 1.42 [1.38-1.48]), and nonroutine discharge. Risk factors for readmission remained consistent at 90 days.

CONCLUSION

Given that nonindex hospital EGS readmission accounts for nearly a quarter of readmissions and often related to important benchmarks such as infection, current EGS quality metrics are inaccurate. This has implications for policy, benchmarking, and readmission reduction programs.

LEVEL OF EVIDENCE

Epidemiological study, level III.

Impact of postdischarge care fragmentation on clinical outcomes and survival following transcatheter aortic valve replacement

BACKGROUND

The study aimed to evaluate the prognostic impact of postdischarge care fragmentation in patients undergoing transcatheter aortic valve replacement (TAVR).

METHODS

A total of 266 patients undergoing TAVR due to severe aortic stenosis were included in this retrospective cohort study. Patients were assigned to one of two groups based on presence (n = 104) and absence (n = 162) of postdischarge care fragmentation. Fragmented care was defined as at least one readmission to a site other than the implanting TAVR center within 90 days. Prognostic impact of care fragmentation on clinical outcomes and predictors of long-term mortality were investigated.

RESULTS

Increased major vascular complication (16.3 vs 8.0%, p = 0.037), permanent pacemaker implantation (14.4 vs 6.2%, p = 0.025), and acute kidney injury (22.1 vs 14.2%, p < 0.001) were reported in the fragmented care group. Although early mortality (6.7 vs 4.3%, p = 0.152) was similar between groups, there was a significant difference in 5‑year mortality (66.3 vs 45.7%, p < 0.001). In a univariate regression analysis fragmented care, age, chronic obstructive pulmonary disease, pulmonary artery systolic pressure, and paravalvular leakage were significantly associated with 5‑year mortality. Fragmented care (hazard ratio [HR] 1.510, 95% confidence interval [CI] 1.080-2.111; p = 0.016), age (HR 1.024, 95% CI 1.001-1.048; p = 0.045), paravalvular leakage (HR 1.863, 95% CI 1.076-3.228; p = 0.026), and chronic obstructive pulmonary disease (HR 1.616, 95% CI 1.114-2.344; p = 0.012) were found to be significant independent predictors of 5‑year mortality in a multivariate analysis, after adjusting for other risks.

CONCLUSION

Fragmented care has a significant prognostic impact on clinical outcomes and survival.

Taking the Long View in an Inpatient Medical Unit: A Person-Centered, Integrated Team Approach for Patients With Severe Mental Illnesses

OBJECTIVE

Patients with severe mental illnesses and related conditions, such as substance misuse and suicide attempts, are among the highest utilizers of acute inpatient medical services. The objective of this study was to assess the impact of a specialized medical unit that uses a comprehensive biopsychosocial model to care for patients with severe mental illnesses.

METHODS

The study used administrative data to compare patients with severe mental illnesses admitted to a specialized unit with patients admitted to medically similar acute (non-intensive care) medical units in a tertiary academic medical center. With controls for sociodemographic variables, illness severity, and medical complexity, multivariate regression analyses compared utilization outcomes for patients from the specialized unit with outcomes from comparison units.

RESULTS

Patients on the specialized unit (N=2,077) were younger, had more mental disorder diagnoses, and were more likely to have less severe general medical illness and less medical complexity than patients from comparison units (N=12,824). Analyses of a subsample of patients with complex behavioral health diagnoses indicated that those on the specialized unit had a shorter average stay, higher odds of discharge to home, and lower odds of 30-day readmission, compared with those on comparison units.

CONCLUSIONS

Specialized units targeted to the needs of patients with serious mental illnesses can provide a moment of engagement when vulnerable patients are likely to benefit from more coordinated care. Findings suggest that a specialized unit that capitalizes on this moment of engagement and uses a biopsychosocial model of care can improve utilization outcomes.

Effects of a standardized community health worker intervention on hospitalization among disadvantaged patients with multiple chronic conditions: A pooled analysis of three clinical trials

OBJECTIVE

To analyze the effects of a standardized community health worker (CHW) intervention on hospitalization.

DATA SOURCES/STUDY SETTING

Pooled data from three randomized clinical trials (n = 1340) conducted between 2011 and 2016.

STUDY DESIGN

The trials in this pooled analysis were conducted across diseases and settings, with a common study design, intervention, and outcome measures. Participants were patients living in high-poverty regions of Philadelphia and were predominantly Medicaid insured. They were randomly assigned to receive usual care versus IMPaCT, an intervention in which CHWs provide tailored social support, health behavior coaching, connection with resources, and health system navigation. Trial one (n = 446) tested two weeks of IMPaCT among hospitalized general medical patients. Trial two (n = 302) tested six months of IMPaCT among outpatients at two academic primary care clinics. Trial three (n = 592) tested six months of IMPaCT among outpatients at academic, Veterans Affairs (VA), and Federally Qualified Health Center primary care practices.

DATA COLLECTION/EXTRACTION METHODS

The primary outcome for this study was all-cause hospitalization, as measured by total number of hospital days per patient. Hospitalization data were collected from statewide or VA databases at 30 days postenrollment in Trial 1, twelve months postenrollment in Trial 2, and nine months postenrollment in Trial 3.

PRINCIPAL FINDINGS

Over 9398 observed patient months, the total number of hospital days per patient in the intervention group was 66 percent of the total in the control group (849 days for 674 intervention patients vs 1258 days for 660 control patients, incidence rate ratio (IRR) 0.66, P < .0001). This reduction was driven by fewer hospitalizations per patient (0.27 vs 0.34, P < .0001) and shorter mean length of stay (4.72 vs 5.57 days, P = .03). The intervention also decreased rates of hospitalization outside patients' primary health system (18.8 percent vs 34.8 percent, P = .0023).

CONCLUSIONS

Data from three randomized clinical trials across multiple settings show that a standardized CHW intervention reduced total hospital days and hospitalizations outside the primary health system. This is the largest analysis of randomized trials to demonstrate reductions in hospitalization with a health system-based social intervention.

Characteristics and clinical outcomes of index versus non-index hospital readmissions in Australian hospitals: a cohort study

Objective Risk factors and clinical outcomes of non-index hospital readmissions (readmissions to a hospital different from the previous admission) have not been studied in Australia. The present study compared characteristics and clinical outcomes between index and non-index hospital readmissions in the Australian healthcare setting. Methods This retrospective cohort study included medical admissions from 2012 to 2016 across all major public hospitals in South Australia. Readmissions within 30 day to all public hospitals were captured using electronic health information system. In-hospital mortality and readmission length of hospital stay (LOS) were compared, along with 30-day mortality and subsequent readmissions among patients readmitted to index or non-index hospitals. Results Of 114105 index admissions, there were 20539 (18.0%) readmissions. Of these, 17519 (85.3%) were index readmissions and 3020 (14.7%) were non-index readmissions. Compared with index readmissions, patients in the non-index readmissions group had a lower Charlson comorbidity index, shorter LOS and fewer complications during the index admission and were more likely to be readmitted with a different diagnosis to the index admission. No difference in in-hospital mortality was observed, but readmission LOS was shorter and 30-day mortality was higher among patients with non-index readmissions. Conclusion A substantial proportion of patients experienced non-index hospital readmissions. Non-index hospital readmitted patients had no immediate adverse outcomes, but experienced worse 30-day outcomes. What is known about the topic? A significant proportion of unplanned hospital readmissions occur to non-index hospitals. North American studies suggest that non-index hospital readmissions are associated with worse outcomes for patients due to discontinuity of care, medical reconciliation and delayed treatment. Limited studies have determined factors associated with non-index hospital readmissions in Australia, but whether such readmissions lead to adverse clinical outcomes is unknown. What does this paper add? In the Australian healthcare setting, 14.7% of patients were readmitted to non-index hospitals. Compared with index hospital readmissions, patients admitted to non-index hospitals had a lower Charlson comorbidity index, a shorter index LOS and fewer complications during the index admission. At the time of readmission there was no differences in discharge summary completion rates between the two groups. Unlike other studies, the present study found no immediate adverse outcomes for patients readmitted to non-index hospitals, but 30-day outcomes were worse than for patients who had an index hospital readmission. What are the implications for practitioners? Non-index hospital readmissions may not be totally preventable due to factors such as ambulance diversions stemming from emergency department overcrowding and prolonged emergency department waiting times. Patients should be advised to re-present to hospital in case they experience recurrence or relapse of a medical condition, and preferably should be readmitted to the same hospital to prevent discontinuity of care.

The Care Transitions Measure-3 Is Only Weakly Associated with Post-discharge Outcomes: a Retrospective Cohort Study in 48,384 Albertans

BACKGROUND

The National Quality Forum endorsed a 3-item Care Transitions Measure (CTM-3), part of the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey, for evaluating hospital care transitions performance.

OBJECTIVE

To explore whether CTM-3 scores are a suitable proxy for quality of transitional care.

DESIGN

Retrospective cohort study.

PARTICIPANTS

A random sample of 48,384 adults discharged from medical or surgical wards in all 113 acute care hospitals in Alberta, Canada, between April 2011 and March 2016.

MAIN MEASURES

CTM-3 scores and their associations with all-cause emergency department (ED) visits or non-elective readmissions at 30 days, 3 months, and 12 months anywhere in the province.

RESULTS

CTM-3 scores were significantly lower (all p < 0.01) for females, older patients, those discharged from medical wards or teaching hospitals, and those with longer length of stay, higher Charlson scores, prior ED visits/hospitalizations, or who did not return to independent living after discharge. CTM-3 scores were not significantly associated with outcomes at 30 days (mean score 77.5 in those who subsequently had an ED visit/readmission vs. 77.9 in those who did not, p = 0.13, aOR 0.99, 95% CI 0.99-0.99). Although CTM-3 scores were significantly lower in patients who subsequently had ED visit/readmission at 3 months (77.5 vs. 78.5) and 12 months (77.6 vs. 79.5), the magnitude of risk was small: for every 10 point decrease in the CTM-3 score, the risk of ED visit/readmission was 2.6% higher (aOR 1.03, 95% CI 1.01-1.05) at 3 months and 4.0% higher (aOR 1.04, 95% CI 1.01-1.08) at 12 months.

CONCLUSIONS

The CTM-3 score is influenced by baseline patient and hospital factors, is not associated with 30-day post-discharge outcomes, and is only weakly associated with 3- and 12-month outcomes. These findings suggest that the CTM-3 score is not a good performance measure for the quality of transitional care.

Using a Social Worker Transition Coach to Improve Hospital-to-Home Transitions for High-Risk Nonelderly Patients

High-risk hospitalized younger adults (age ≤60) have 30-day readmission rates comparable to Medicare fee-for-service patients. This younger cohort has a high incidence of comorbid mental health and substance use disorders, which increases the complexity of their postdischarge care. Although few care transition studies have enrolled younger adult patients, findings from our previous work suggest that these patients have postdischarge needs requiring different approaches than those serving elderly patients. Our current pilot study, situated in a safety-net system, targets this younger population, employing a social worker as the Transition Coach (TC). Social workers are explicitly trained to address psychosocial complexities, and we evaluated whether our TC intervention could improve hospital-to-home transitions by assisting patients with medication management, attending follow-up appointments, and addressing medical, psychiatric, and psychosocial needs. Primary outcomes were Patient Activation Measure scores on admission and 30-days postdischarge; outpatient follow-up at 7 and 30 days; and all-cause, in-network 30-, 60-, and 90-day readmissions. At 30 and 60 days, no differences were observed in the primary outcomes; at 90 days, intervention patients demonstrated a trend toward readmission reduction. A social worker-led transitional care program shows promise in reducing readmissions over 90 days among high-risk, lower socioeconomic, nonelderly adult patients.

Individual and Organizational Factors Associated With Hospital Readmission Rates: Evidence From a U.S. National Sample

Objective: Hospital readmission rate is an important indicator for assessing quality of care in the acute and postacute settings. Identifying factors that increase risk for hospital readmissions can aid in the recognition of potential targets for quality improvement efforts. The main objective of this brief report was to examine the factors that predict increased risk of 30-day readmissions. Method: We analyzed data from the 2013 National Readmission Database (NRD). Results: The main factors that predicted increased risk of 30-day readmission were number of chronic conditions, severity of illness, mortality risk, and hospital ownership. Unexpectedly, discharge from a for-profit hospital was associated with greater risk for hospital readmission in the United States. Discussion and Conclusion: These findings suggest that patients with severe physical illness and multiple chronic conditions should be the primary targets for hospital transitional care interventions to help reduce the rate of unnecessary hospital readmissions.

Hidden Costs of Hospitalization After Firearm Injury: National Analysis of Different Hospital Readmission

OBJECTIVE

To compare the risk factors and costs associated with readmission after firearm injury nationally, including different hospitals.

BACKGROUND

No national studies capture readmission to different hospitals after firearm injury.

METHODS

The 2013 to 2014 Nationwide Readmissions Database was queried for patients admitted after firearm injury. Logistic regression identified risk factors for 30-day same and different hospital readmission. Cost was calculated. Survey weights were used for national estimates.

RESULTS

There were 45,462 patients admitted for firearm injury during the study period. The readmission rate was 7.6%, and among those, 16.8% were readmitted to a different hospital. Admission cost was $1.45 billion and 1-year readmission cost was $131 million. Sixty-four per cent of those injured by firearms were publicly insured or uninsured. Readmission predictors included: length of stay >7 days [odds ratio (OR) 1.43, P < 0.01], Injury Severity Score >15 (OR 1.41, P < 0.01), and requiring an operation (OR 1.40, P < 0.01). Private insurance was a predictor against readmission (OR 0.81, P < 0.01). Predictors of readmission to a different hospital were unique and included: initial admission to a for-profit hospital (OR 1.52, P < 0.01) and median household income ≥$64,000 (OR 1.48, P < 0.01).

CONCLUSIONS

A significant proportion of the national burden of firearm readmissions is missed by not tracking different hospital readmission and its unique set of risk factors. Firearm injury-related hospitalization costs $791 million yearly, with the largest fraction paid by the public. This has implications for policy, benchmarking, quality, and resource allocation.

Cohort study of risk factors for 30-day readmission after abdominal aortic aneurysm repair

Background: We conducted a retrospective cohort study of thirty-day readmission after abdominal aortic aneurysm (AAA) repair. Patients and methods: Inpatients (2009-2016) undergoing elective AAA repair were selected from the multicenter Cerner Health Facts® database using ICD-9 procedure codes. We identified characteristics associated with 30-day readmission with chi-square analysis and logistic regression. Results: 4,723 patients undergoing elective AAA procedures were identified; 3,101 endovascular aneurysm repairs (EVAR) and 1,622 open procedures. Readmission differed by procedure type (6.5 % EVAR vs. 9.3 % open, p =.0005). Multivariable logistic regression found that patients undergoing EVAR were less likely to be readmitted (OR 0.71, 95 % CI 0.54-0.92) than patients undergoing open repair. The following risk factors were associated with 30-day readmission following any AAA repair: surgical site infection during the index admission (OR 2.79, 95 % CI 1.25-6.22), age (OR 1.03, 95 % CI 1.01-1.05), receipt of bronchodilators (OR 1.34, 95 % CI 1.06-1.70) or steroids (OR 1.45, 95 % CI 1.04-2.02), serum potassium > 5.2 mEq/L (OR 1.89, 95 % CI 1.16-3.06), and higher Charlson co-morbidity scores (OR 1.12, 95 % CI 1.04-1.21). Subgroup analysis revealed that age (OR 1.02, 95 % CI 1.01-1.04), higher Charlson comorbidity scores (OR 1.20, 95 % CI 1.09-1.33), and receipt of post-operative bronchodilators (OR 1.39, 95 % CI 1.03-1.88) were risk factors for 30-day readmission following EVAR. After open procedures, readmission was associated with surgical site infection during the index admission (OR 2.91, 95 % CI 1.17-7.28), chronic heart failure (OR 2.18, 95 % CI 1.22-3.89), and receipt of post-operative steroids (OR 1.92, 95 % CI 1.24-2.96). The most common infections were pneumonia after open procedures and urinary tract infection after EVAR. Conclusions: The risk factor most associated with 30-day readmission after elective AAA repair was surgical site infection. Awareness of these risk factors and vulnerable groups may help identify high-risk patients who could benefit from increased surveillance programs to reduce readmission.

Reducing excess hospital readmissions: Does destination matter?

Reducing excess hospital readmissions has become a high policy priority to lower health care spending and improve quality. The Affordable Care Act (ACA) penalizes hospitals with higher-than-expected readmission rates. This study tracks patient-level admissions and readmissions to Florida hospitals from 2006 to 2014 to examine whether the ACA has reduced readmission effectively. We compare not only the change in readmissions in targeted conditions to that in non-targeted conditions, but also changes in sites of readmission over time and differences in outcomes based on destination of readmission. We find that the drop in readmissions is largely owing to the decline in readmissions to the original hospital where they received operations or treatments (i.e., the index hospital). Patients readmitted into a different hospital experienced longer hospital stays. The results suggest that the reduction in readmission is likely achieved via both quality improvement and strategic admission behavior.

Impact of a Patient Navigator Program on Hospital-Based and Outpatient Utilization Over 180 Days in a Safety-Net Health System

BACKGROUND

With emerging global payment structures, medical systems need to understand longer-term impacts of care transition strategies.

OBJECTIVE

To determine the effect of a care transition program using patient navigators (PNs) on health service utilization among high-risk safety-net patients over a 180-day period.

DESIGN

Randomized controlled trial conducted October 2011 through April 2013.

PARTICIPANTS

Patients admitted to the general medicine service with ≥1 readmission risk factor: (1) age ≥ 60; (2) in-network inpatient admission within prior 6 months; (3) index length of stay ≥ 3 days; or (4) admission diagnosis of heart failure or (5) chronic obstructive pulmonary disease. The analytic sample included 739 intervention patients, 1182 controls.

INTERVENTIONS

Through hospital visits and 30 days of post-discharge telephone outreach, PNs provided coaching and assistance with medications, appointments, transportation, communication with primary care, and self-care.

MAIN MEASURES

Primary outcomes: (1) hospital-based utilization, a composite of ED visits and hospital admissions; (2) hospital admissions; (3) ED visits; and (4) outpatient visits. We evaluated outcomes following an index discharge, stratified by patient age (≥ 60 and < 60 years), using a 180-day time frame divided into six 30-day periods.

KEY RESULTS

The PN program produced starkly different outcomes by patient age. Among older PN patients, hospital-based utilization was consistently lower than controls, producing an 18.7% cumulative decrease at 180 days (p = 0.038); outpatient visits increased in the critical first 30-day period (p = 0.006). Among younger PN patients, hospital-based utilization was 31.7% (p = 0.038) higher at 180 days, largely reflecting sharply higher utilization in the initial 30 days (p = 0.002), with non-significant changes thereafter; outpatient visits experienced no significant changes.

CONCLUSIONS

A PN program serving high-risk safety-net patients differentially impacted patients based on age, and among younger patients, outcomes varied over time. Our findings highlight the importance for future research to evaluate care transition programs among different subpopulations and over longer time periods.

Patients With Heart Failure Readmitted to the Original Hospital Have Better Outcomes Than Those Readmitted Elsewhere

Background

Up to one fifth of readmissions after a heart failure hospitalization occur at a different hospital. This negatively impacts information continuity, but whether site of readmission impacts subsequent outcomes is unclear.

Methods and Results

Retrospective cohort study of all patients discharged with a primary diagnosis of heart failure in Canada between April 2004 and December 2013. We compared patients readmitted within 30 days to the original hospital versus a different hospital. Of the 217 039 heart failure patients (mean age, 76.8 years, 50.1% male), 39 368 (18.1%) were readmitted within 30 days—32 771 (83.2%) to the original hospital and 6597 (16.8%) to a different hospital (increasing over time from 15.6% in 2004 to 18.5% by 2013; P for trend=0.001). Patients readmitted to different hospitals were younger and were more likely to be male, have a rural residence, a more‐recent discharge year, an index hospitalization at a teaching hospital, and to be brought in by ambulance at the time of the readmission. Readmissions to the original hospital were substantially shorter (mean, 10.4 days [95% CI, 10.3–10.6] versus 11.6 days [95% CI, 11.3–12.0]; adjusted means, 11.0 versus 12.0; P<0.0001) and had lower mortality (14.4% versus 15.0%; adjusted odds ratio, 0.89; 95% CI, 0.82–0.96) than readmissions to different hospitals.

Conclusions

Readmissions to a different hospital are becoming more frequent over time and are associated with longer stays and higher mortality rates than readmissions to the original hospital. Our findings provide further evidence that care fragmentation may be deleterious for patients with heart failure.

Health information exchange systems and length of stay in readmissions to a different hospital

BACKGROUND

Readmission to a different hospital than the original discharge hospital may result in breakdowns in continuity of care. In different-hospital readmissions (DHRs), continuity can be maintained when hospitals are connected through health information exchange (HIE) systems.

OBJECTIVE

To examine whether length of readmission stay (LORS) differs between same-hospital readmissions and DHRs, and whether in DHRs the LORS differs by the availability of HIE.

DESIGN

A retrospective cohort study of all internal medicine 30-day readmissions in 27 Israeli hospitals between January 1, 2010 and December 31, 2010.

SETTING

Clalit Health Services-Israel's largest integrated healthcare provider and payer.

POPULATION

Adult Clalit members (aged 18 and older) with at least 1 readmission during the study period.

METHODS

A multivariate marginal Cox model tested the likelihood for discharge during each readmission day in same-hospital readmissions (SHRs), DHRs with HIE, and DHRs without HIE.

RESULTS

Of the 27,057 readmissions, 3130 (11.6%) were DHRs and 792 where DHRs with HIE in both the index and readmitting hospital. Partial continuity (DHRs with HIE) was associated with decreased likelihood of discharge on any given day compared with full continuity (SHRs) (hazard ratio [HR] = 0.85, 95% confidence interval [CI]: 0.79-0.91). Similar results were obtained for no continuity (DHRs without HIE) versus full continuity (HR = 0.90, 95% CI: 0.86-0.94). The difference between DHRs with and without HIE was not significant.

CONCLUSIONS

The prolonged LORS in DHRs versus SHRs was not mitigated by the existence of HIE systems. Future research is needed to further elucidate the effects of actual use of HIE on length of DHRs. Journal of Hospital Medicine 2016;11:401-406. © 2015 Society of Hospital Medicine.

Readmissions After Colon Cancer Surgery: Does It Matter Where Patients Are Readmitted?

PURPOSE

Readmissions to a different hospital may place patients at increased risk for poor outcomes and may increase their overall costs of care. We evaluated whether mortality and costs differ for patients with colon cancer on the basis of whether patients are readmitted to the index hospital or to a different hospital within 30 days of discharge.

METHODS

We conducted a retrospective analysis using SEER-Medicare linked claims data for patients with stage I to III colon cancer diagnosed between 2000 and2009 who were readmitted within 30 days (N = 3,399). Our primary outcome was all-cause mortality, which was modeled by using Cox proportional hazards. Secondary outcomes included colon cancer-specific mortality, 90-day mortality, and costs of care. We used subhazard ratios for colon cancer- specific mortality and generalized linear models for costs. For each model, we used a propensity score-weighted doubly robust approach to adjust for patient, physician, and hospital characteristics.

RESULTS

Approximately 23% (n = 769) of readmitted patients were readmitted to a different hospital than where they were initially discharged. After adjustment, there was no difference in all-cause mortality, colon cancer-specific mortality, or cost of care for patients readmitted to a different hospital. Patient readmitted to a different hospital did have a higher risk of short-term mortality (90-day all-cause mortality; adjusted hazard ratio, 1.18; 95% CI, 1.02 to 1.38).

CONCLUSION

Readmission to a different hospital after colon cancer surgery is associated with short-term mortality but not with long-term mortality nor with post-discharge costs of care. Additional investigation is needed to determine how to improve short-term mortality among patients readmitted to different hospitals.

Fragmentation of Care after Surgical Discharge: Non-Index Readmission after Major Cancer Surgery

BACKGROUND

Despite national emphasis on care coordination, little is known about how fragmentation affects cancer surgery outcomes. Our study examines a specific form of fragmentation in post-discharge care-readmission to a hospital different from the location of the operation-and evaluates its causes and consequences among patients readmitted after major cancer surgery.

STUDY DESIGN

We used the State Inpatient Database of California (2004 to 2011) to identify patients who had major cancer surgery and their subsequent readmissions. Logistic models were used to examine correlates of non-index readmissions and to assess associations between location of readmission and outcomes, measured by in-hospital mortality and repeated readmission.

RESULTS

Of 9,233 readmissions within 30 days of discharge after major cancer surgery, 20.0% occurred in non-index hospitals. Non-index readmissions were associated with emergency readmission (odds ratio [OR] = 2.63; 95% CI, 2.26-3.06), rural residence (OR = 1.81; 95% CI, 1.61-2.04), and extensive procedures (eg hepatectomy vs proctectomy; OR = 2.77; CI, 2.08-3.70). Mortality was higher during non-index readmissions than index readmissions independent of patient, procedure, and hospital factors (OR = 1.31; 95% CI, 1.03-1.66), but was mitigated by adjusting for conditions present at readmission (OR = 1.24; 95% CI, 0.98-1.58). Non-index readmission predicted higher odds of repeated readmission within 60 days of discharge from the first readmission (OR = 1.16; 95% CI, 1.02-1.32), independent of all covariates.

CONCLUSIONS

Non-index readmissions constitute a substantial proportion of all readmissions after major cancer surgery. They are associated with more repeated readmissions and can be caused by severe surgical complications and increased travel burden. Overcoming disadvantages of non-index readmissions represents an opportunity to improve outcomes for patients having major cancer surgery.

Predictors and outcomes of unplanned readmission to a different hospital

OBJECTIVES

To examine patient, hospital and market factors and outcomes associated with readmission to a different hospital compared with the same hospital.

DESIGN

A population-based, secondary analysis using multilevel causal modeling.

SETTING

Acute care hospitals in California in the USA.

PARTICIPANTS

In total, 509 775 patients aged 50 or older who were discharged alive from acute care hospitals (index hospitalizations), and 59 566 who had a rehospitalization within 30 days following their index discharge.

INTERVENTION

No intervention.

MAIN OUTCOME MEASURE(S)

Thirty-day unplanned readmissions to a different hospital compared with the same hospital and also the costs and health outcomes of the readmissions.

RESULTS

Twenty-one percent of patients with a rehospitalization had a different-hospital readmission. Compared with the same-hospital readmission group, the different-hospital readmission group was more likely to be younger, male and have a lower income. The index hospitals of the different-hospital readmission group were more likely to be smaller, for-profit hospitals, which were also more likely to be located in counties with higher competition. The different-hospital readmission group had higher odds for in-hospital death (8.1 vs. 6.7%; P < 0.0001) and greater readmission hospital costs ($15 671.8 vs. $14 286.4; P < 0.001) than the same-hospital readmission group.

CONCLUSIONS

Patient, hospital and market characteristics predicted different-hospital readmissions compared with same-hospital readmissions. Mortality and cost outcomes were worse among patients with different-hospital readmissions. Strategies for better care coordination targeting people at risk for different-hospital readmissions are necessary.

Same-Hospital Readmission Rates as a Measure of Pediatric Quality of Care

IMPORTANCE

Health care systems, payers, and hospitals use hospital readmission rates as a measure of quality. Although hospitals can track readmissions back to themselves (hospital A to hospital A), they lack information when their patients are readmitted to different hospitals (hospital A to hospital B). Because hospitals lack different-hospital readmission (DHR) data, they may underestimate all-hospital readmission (AHR) rates (hospital A to hospital A or B).

OBJECTIVES

To determine the prevalence of 30-day pediatric DHRs; to assess the effect of DHR on readmission performance; and to identify patient and hospital characteristics associated with DHR.

DESIGN, SETTING, AND PARTICIPANTS

We analyzed all-payer inpatient claims for 701,263 pediatric discharges (patients aged 0-17 years) from 177 acute care hospitals in New York State from January 1, 2005, through November 30, 2009, to identify 30-day same-hospital readmissions (SHRs), DHRs, and AHRs. Data analysis was performed from March 12, 2013, through April 6, 2015. We compared excess readmission ratios (calculated per the Medicare formula) using SHRs and AHRs to determine what might happen if the federal formula were applied to a specific state and to evaluate how often hospitals might accurately anticipate-using data available to them--whether they would incur penalties (excess readmission ratio >1) for readmissions. Using multivariate logistic regression, we identified patient- and hospital-level predictors of DHR vs SHR.

MAIN OUTCOMES AND MEASURES

The proportion of DHRs vs SHRs, AHR and SHR rates, and excess readmissions.

RESULTS

Different-hospital readmissions constituted 13.9% of 31,325 AHRs. At the individual hospital level, the median (interquartile range) percentage of DHRs was 21.6% (12.8%-39.1%). The median (interquartile range) adjusted AHR rate was 3.4% (3.0%-4.1%), 38.9% higher than the median adjusted SHR rate of 2.5% (2.0%-3.4%) (P < .001). Excess readmission ratios using SHRs inaccurately anticipated penalties (changed from >1 to ≤ 1 or vice versa) for 20 of the 177 hospitals (11.3%); all were nonchildren's hospitals and 18 of 20 (90.0%) were nonteaching hospitals. Characteristics associated with higher odds ratios (ORs) (reported with 95% CIs) of DHR in multivariate analyses included being younger (compared with age <1 year, ORs [95% CIs] for the other age categories ranged from 0.76 [0.66-0.88] to 0.85 [0.73-0.99]); being white (ORs [95% CIs] for nonwhite race/ethnicity ranged from 0.74 [0.65-0.84] to 0.88 [0.79-0.99]); having private insurance (1.14 [1.04-1.24]); having a chronic condition indicator for a mental disorder (1.33 [1.13-1.56]) or a disease of the nervous system (1.37 [1.20-1.57]) or circulatory system (1.20 [1.00-1.43]); and admission to a nonchildren's (1.62 [1.01-2.60]), urban (ORs for nonurban hospitals ranged from 0.35 [0.24-0.52] to 0.36 [0.21-0.64]), or lower-volume (0.73 [0.64-0.84]) hospital (P < .05 for each).

CONCLUSIONS AND RELEVANCE

Different-hospital readmissions differentially affect hospitals' pediatric readmission rates and anticipated performance, making SHRs an incomplete surrogate for AHRs-particularly for certain hospital types. Failing to incorporate DHRs into readmission measurement may impede quality assessment, anticipation of penalties, and quality improvement.

The Quality of Surgical and Pneumonia Care in Minority-Serving and Racially Integrated Hospitals

OBJECTIVE

To explore the association between quality of care for surgical and pneumonia patients and the racial/ethnic composition of hospitals' patients.

DATA SOURCE

Our primary data were surgical and pneumonia processes of care indicators from the 2012 Medicare Hospital Compare Data. We merged this data with information from the 2011 American Hospital Association Annual Survey of Hospitals. We computed the racial and ethnic composition of hospital patients using 2008 data from the Healthcare Costs and Utilization Project.

STUDY DESIGN

The sample included 1,198 acute care general hospitals from 11 states: AZ, CA, FL, IA, MA, MD, NC, NJ, NY, WA, and WI. We compared quality across minority-serving, racially integrated, and majority-white hospitals using unconditional quantile regression models controlling for hospital and market characteristics.

PRINCIPAL FINDINGS

We found quality differences between the lowest performing minority-serving, racially integrated, and majority-white hospitals. As we moved from 10th to 90th quantile, the quality differences between hospitals by patients' racial composition disappeared. In other words, the best minority-serving and racially integrated hospitals performed as well as the best majority hospitals.

CONCLUSIONS

Efforts to improve quality of care for patients in minority-serving and racially integrated hospitals should focus on the lowest performers.

Contextual, organizational and ecological effects on the variations in hospital readmissions of rural Medicare beneficiaries in eight southeastern states

The enactment of the Patient Protection and Affordable Care Act (ACA) has been expected to improve the coverage of health insurance, particularly as related to the coordination of seamless care and the continuity of elder care among Medicare beneficiaries. The analysis of longitudinal data (2007 through 2013) in rural areas offers a unique opportunity to examine trends and patterns of rural disparities in hospital readmissions within 30 days of discharge among Medicare beneficiaries served by rural health clinics (RHCs) in the eight southeastern states of the Department of Health & Human Services (DHHS) Region 4. The purpose of this study is twofold: first, to examine rural trends and patterns of hospital readmission rates by state and year (before and after the ACA enactment); and second, to investigate how contextual (county characteristic), organizational (clinic characteristic) and ecological (aggregate patient characteristic) factors may influence the variations in repeat hospitalizations. The unit of analysis is the RHC. We used administrative data compiled from multiple sources for the Centers of Medicare and Medicaid Services for a period of seven years. From 2007 to 2008, risk-adjusted readmission rates increased slightly among Medicare beneficiaries served by RHCs. However, the rate declined in 2009 through 2013. A generalized estimating equation of sixteen predictors was analyzed for the variability in risk-adjusted readmission rates. Nine predictors were statistically associated with the variability in risk-adjusted readmission rates of the RHCs pooled from 2007 through 2013 together. The declined rates were associated with by the ACA effect, Georgia, North Carolina, South Carolina, and the percentage of elderly population in a county where RHC is located. However, the increase of risk-adjusted rates was associated with the percentage of African Americans in a county, the percentage of dually eligible patients, the average age of patients, and the average clinical visits by African American patients. The sixteen predictors accounted for 21.52 % of the total variability in readmissions. This study contributes to the literature in health disparities research from the contextual, organizational and ecological perspectives in the analysis of longitudinal data. The synergism of multiple contextual, organizational and ecological factors, as shown in this study, should be considered in the design and implementation of intervention studies to address the problem of hospital readmissions through prevention and enhancement of disease management of rural Medicare beneficiaries.

A Patient Navigator Intervention to Reduce Hospital Readmissions among High-Risk Safety-Net Patients: A Randomized Controlled Trial

BACKGROUND

Evidence-based interventions to reduce hospital readmissions may not generalize to resource-constrained safety-net hospitals.

OBJECTIVE

To determine if an intervention by patient navigators (PNs), hospital-based Community Health Workers, reduces readmissions among high risk, low socioeconomic status patients.

DESIGN

Randomized controlled trial.

PARTICIPANTS

General medicine inpatients having at least one of the following readmission risk factors: (1) age ≥60 years, (2) any in-network inpatient admission within the past 6 months, (3) length of stay ≥3 days, (4) admission diagnosis of heart failure, or (5) chronic obstructive pulmonary disease. The analytic sample included 585 intervention patients and 925 controls.

INTERVENTIONS

PNs provided coaching and assistance in navigating the transition from hospital to home through hospital visits and weekly telephone outreach, supporting patients for 30 days post-discharge with discharge preparation, medication management, scheduling of follow-up appointments, communication with primary care, and symptom management.

MAIN MEASURES

The primary outcome was in-network 30-day hospital readmissions. Secondary outcomes included rates of outpatient follow-up. We evaluated outcomes for the entire cohort and stratified by patient age >60 years (425 intervention/584 controls) and ≤60 years (160 intervention/341 controls).

KEY RESULTS

Overall, 30-day readmission rates did not differ between intervention and control patients. However, the two age groups demonstrated marked differences. Intervention patients >60 years showed a statistically significant adjusted absolute 4.1% decrease [95% CI: -8.0%, -0.2%] in readmission with an increase in 30-day outpatient follow-up. Intervention patients ≤60 years showed a statistically significant adjusted absolute 11.8% increase [95% CI: 4.4%, 19.0%] in readmission with no change in 30-day outpatient follow-up.

CONCLUSIONS

A patient navigator intervention among high risk, safety-net patients decreased readmission among older patients while increasing readmissions among younger patients. Care transition strategies should be evaluated among diverse populations, and younger high risk patients may require novel strategies.

Diabetes care and outcomes for American Indians and Alaska natives in commercial integrated delivery systems: a SUrveillance, PREvention, and ManagEment of Diabetes Mellitus (SUPREME-DM) Study

OBJECTIVE

To compare cardiovascular disease risk factor testing rates and intermediate outcomes of care between American Indian/Alaska Native (AI/AN) patients with diabetes and non-Hispanic Caucasians enrolled in nine commercial integrated delivery systems in the USA.

RESEARCH DESIGN AND METHODS

We used modified Poisson regression models to compare the annual testing rates and risk factor control levels for glycated haemoglobin (HbA1c), low-density lipoprotein cholesterol (LDL-C), and systolic blood pressure (SBP); number of unique diabetes drug classes; insulin use; and oral diabetes drug medication adherence between insured AI/AN and non-Hispanic white adults with diabetes aged ≥18 in 2011.

RESULTS

5831 AI/AN patients (1.8% of the cohort) met inclusion criteria. After adjusting for age, gender, comorbidities, insulin use, and geocoded socioeconomic status, AI/AN patients had similar rates of annual HbA1c, LDL-C, and SBP testing, and LDL-C and SBP control, compared with non-Hispanic Caucasians. However, AI/AN patients were significantly more likely to have HbA1c >9% (>74.9 mmol/mol; RR=1.47, 95% CI 1.38 to 1.58), and significantly less likely to adhere to their oral diabetes medications (RR=0.90, 95% CI 0.88 to 0.93) compared with non-Hispanic Caucasians.

CONCLUSIONS

AI/AN patients in commercial integrated delivery systems have similar blood pressure and cholesterol testing and control, but significantly lower rates of HbA1c control and diabetes medication adherence, compared with non-Hispanic Caucasians. As more AI/ANs move to urban and suburban settings, clinicians and health plans should focus on addressing disparities in diabetes care and outcomes in this population.

Readmissions after complex aneurysm repair are frequent, costly, and primarily at nonindex hospitals

OBJECTIVE

Readmissions after complex vascular surgery are not well studied. We sought to determine the rate of readmission after thoracic and thoracoabdominal aortic aneurysm repair (TAA/TAAAR) at our institution and to identify risk factors for and costs of readmission.

METHODS

Using a prospectively collected institutional database in conjunction with a Maryland statewide database, we reviewed index admissions and early readmissions for all patients who underwent TAA/TAAAR between 2002 and 2013 at the Johns Hopkins Hospital. Only Maryland residents were included to capture readmissions to any Maryland hospital.

RESULTS

We identified 115 Maryland residents (58% men; mean age, 65 ± 1.2 years) undergoing TAA/TAAAR (57% open repair). Early readmissions were frequent and occurred in 29% of patients. Of the readmitted patients, 79% (P < .001) were not readmitted to the index hospital where their operation was performed. Readmitted patients were not significantly different from nonreadmitted patients in age, gender, race, aneurysm type, and index length of stay. They were not different in preoperative comorbidities (including coronary artery disease, diabetes mellitus, smoking, renal insufficiency, and pulmonary disease), postoperative neurologic, renal, and cardiovascular complications, or 30-day or 5-year mortality. Multivariable analysis showed that significant risk factors for readmission were open repair (odds ratio, 3.12; 95% confidence interval, 1.12-9.54; P = .03) and postoperative pneumonia (odds ratio, 4.31; 95% confidence interval, 1.28-15.4; P = .02). Readmitted patients had significantly lower average income compared with the nonreadmitted cohort (U.S. $62,000 ± $4000 vs $73,000 ± $3000; P = .04). Striking differences were seen between patients readmitted to the index hospital where the operation was performed, and those who were readmitted to a nonindex hospital: patients readmitted to the index hospital were readmitted mainly for aneurysm-related surgical issues, whereas patients readmitted to the nonindex hospital were readmitted for medical morbidities. An aneurysm-related intervention was required in 75% of patients readmitted to the index hospital vs in 9% of patients readmitted to the nonindex hospital. Readmissions to a nonindex hospital cost significantly less than to the index hospital (U.S. $20,000 ± $4400 vs $42,000 ± $8800; P = .03) and were not associated with increased overall mortality.

CONCLUSIONS

Early readmissions after TAA/TAAA repair are frequent and often occur at hospitals other than the index institution. Risk factors for readmission include open repair and postoperative pneumonia but not pre-existing patient comorbidities. Readmissions to nonindex hospitals were related to medical morbidities that were associated with fewer interventions and lower costs compared with the index hospital. Focusing on preoperative risk factors in this group of patients may not lead to reduction in readmissions. Minimizing nonsurgical complications may reduce post-TAA/TAAAR readmissions and the high costs associated with repeat care.

Postdischarge complications are an important predictor of postoperative readmissions

BACKGROUND

Thirty-day readmissions are common in general surgery patients and affect long-term outcomes including mortality. We sought to determine the effect of complication timing on postoperative readmissions.

METHODS

Patients from our institutional American College of Surgeons National Surgical Quality Improvement Project database who underwent general surgery procedures from 2006 to 2011 were included. The primary outcome of interest was 30-day hospital readmission.

RESULTS

Patients diagnosed with postdischarge complications were significantly more likely to be readmitted (56%) compared with patients diagnosed with complications before discharge (7%, P < .001). Independent predictors of postdischarge complications included laparoscopic case, short hospital stay, preoperative dyspnea, and independent functional status. Gastrointestinal complications and surgical site infection were the most common reasons for readmission.

CONCLUSIONS

The development of complications after hospital discharge places patients at significant risk for readmission. Early identification and treatment of gastrointestinal complications and surgical site infections in the outpatient setting may decrease postoperative readmission rates.

Site of hospital readmission and mortality: a population-based retrospective cohort study

BACKGROUND

Unplanned hospital readmission is a complex process, particularly if the patient is readmitted to an acute care institution other than the original hospital. This study tested the hypothesis that readmission to an alternative hospital is associated with increased mortality compared with readmission to the original hospital.

METHODS

We performed a population-based retrospective cohort analysis set between 1995 and 2010 for all 21 acute care adult general hospitals in the Greater Toronto and Hamilton Area. Participants were consecutive adults (age ≥ 18 yr) readmitted through the emergency department within 30 days after hospital discharge. The primary outcome measure was all-cause mortality within 30 days after readmission.

RESULTS

Of the 198 149 patients included in the study, 38 134 (19.2%) died within 30 days after readmission. Patients readmitted to an alternative hospital were more likely than those readmitted to the original hospital to be older, reside in a chronic-care facility and arrive by ambulance. Alternative-hospital readmission was associated with a higher risk of death within 30 days (22.3% v. 18.6%, p < 0.001; odds ratio [OR] 1.26, 95% confidence interval [CI] 1.23-1.30). The increased risk was substantially less after adjustment for patient- and hospital-level covariables (adjusted OR 1.06, 95% CI 1.02-1.10). Unadjusted Kaplan-Meier survival curves separated early and the absolute difference in mortality continued throughout the entire 1-year follow-up period, but no difference between groups was observed based on adjusted survival analyses.

INTERPRETATION

Among patients readmitted within 30 days after discharge, readmission to an alternative hospital was associated with a higher risk of death than readmission to the original hospital. Whether this adverse prognosis reflects a true causal relation or residual confounding is unknown.

Rehospitalization to primary versus different facilities following abdominal aortic aneurysm repair

OBJECTIVE

Reducing readmissions represents a unique opportunity to improve care and reduce health care costs and is the focus of major payers. A large number of surgical patients are readmitted to hospitals other than where the primary surgery was performed, resulting in clinical decisions that do not incorporate the primary surgeon and potentially alter outcomes. This study characterizes readmission to primary vs different hospitals after abdominal aortic aneurysm (AAA) repair and examines the implications with regard to mortality and cost.

METHODS

Patients who underwent open or endovascular aneurysm repair for AAA were identified from the Centers for Medicare and Medicaid Services Chronic Conditions Warehouse, a random 5% national sample of Medicare beneficiaries from 2005 to 2009. Outcomes for patients who underwent AAA repair and were readmitted within 30 days of initial discharge were compared based on readmission location (primary vs different hospital).

RESULTS

A total of 885 patients underwent AAA repair and were readmitted within 30 days. Of these, 626 (70.7%) returned to the primary facility, and 259 (29.3%) returned to a different facility. Greater distance from patient residence to the primary hospital was the strongest predictor of readmission to a different facility. Patients living 50 to 100 miles from the primary hospital were more likely to be readmitted to a different hospital compared with patients living <10 miles away (odds ratio, 8.50; P < .001). Patients with diagnoses directly related to the surgery (eg, wound infection) were more likely to be readmitted to the primary hospital, whereas medical diagnoses (eg, pneumonia and congestive heart failure) were more likely to be treated at a different hospital. There was no statistically significant difference in mortality between patients readmitted to a different or the primary hospital. Median total 30-day payments were significantly lower at different vs primary hospitals (primary, $11,978 vs different, $11,168; P = .04).

CONCLUSIONS

Readmission to a different facility after AAA repair is common and occurs more frequently than for the overall Medicare population. Patients travelling a greater distance for AAA repair are more likely to return to different vs the primary hospital when further care is required. For AAA repair, quality healthcare may be achieved at marginally lower cost and with greater patient convenience for selected readmissions at hospitals other than where the initial procedure was performed.

Evaluation of readmission rates for carotid endarterectomy versus carotid artery stenting in the US Medicare population

OBJECTIVE

We evaluated rates and identified predictors of readmission in the Medicare population after carotid endarterectomy (CEA) compared to carotid artery stenting (CAS).

METHODS

MedPAR data (2005-2009) were used to select patients who underwent CEA or CAS (utilizing International Classification of Diseases, Ninth Revision, Clinical Modification codes). Readmission was evaluated using chi-square and multivariable logistic regression.

RESULTS

A total of 235 247 carotid interventions were performed (211 118 CEA and 24 129 CAS). Readmission rates (%) for patients undergoing CEA and CAS, respectively, were 8.84 and 11.11 (30 days; P < .0001); 13.31 and 17.98 (60 days; P < .0001); and 16.86 and 22.68 (90 days; P < .0001). Patients aged >80 (odds ratio [OR] = 1.25; 95% confidence interval [CI] = 1.20-1.30) and patients with renal failure (OR = 1.6 95%; CI = 1.56-1.73), congestive heart failure (OR = 1.6; 95%CI = 1.57-1.73), diabetes (OR = 1.4; 95% CI 1.27-1.52), and CAS (OR = 1.2; 95%CI = 1.15-1.25) were more likely to be readmitted.

CONCLUSIONS

Interventions for carotid artery disease had high overall readmission rates. After adjustment for comorbidities, utilization of less invasive techniques (CAS) did not result in lower readmission rates. Further evaluation is needed to determine strategies to reduce hospital readmission rates after carotid interventions.

Risk factors for readmission of orthopaedic surgical patients

BACKGROUND

Reducing hospital readmissions has become a priority in the development of policies aimed at patient safety and cost reduction. Evaluating the incidence of rehospitalization of orthopaedic surgical patients could help to identify targets for more efficient perioperative care. We addressed two questions: What is the incidence of thirty-day readmission for orthopaedic patients at an academic hospital? Can any risk factors for readmission be identified among rehospitalized patients?

METHODS

This is a retrospective cohort study examining 3264 orthopaedic surgical admissions during two fiscal years from the hospital's quality-improvement database. Cases of patients with unplanned readmission within thirty days were subjected to univariate and multivariate analysis to determine the odds ratio (OR) for readmission. Further descriptive analysis was performed with use of electronic medical record data from the cohort of readmitted patients.

RESULTS

The estimated cumulative incidence of unplanned thirty-day readmissions was 4.2% (i.e., 138 of the 3261 patients who were eligible for the study). Multivariate analysis indicated that marital status of "widowed" significantly increased the risk of readmission (OR, 1.846; 95% confidence interval [CI], 1.070 to 3.184; p = 0.03). Race significantly increased the odds of readmission in patients identified as African-American (OR, 2.178; 95% CI, 1.077 to 4.408; p = 0.03), or American Indian or Alaskan Native race (OR, 3.550; 95% CI, 1.429 to 8.815; p = 0.006). The risk of readmission was significant at p &lt; 0.10 (OR 1.547; 95% CI, 0.941 to 2.545; p = 0.09) for patients with Medicaid insurance. Any intensive care unit stay gave the highest OR of readmission (OR, 2.356; 95% CI, 1.361 to 4.079; p = 0.002) for all demographic groups. Mean length of hospital stay was significantly longer, 5.9 days in the unplanned readmission group compared with 3.6 days for non-readmitted patients (OR, 1.038; 95% CI, 1.014 to 1.062; p = 0.002). Chart review of readmitted patients showed that 102 readmissions (73.9%) were classified as surgical; of these, thirty-five readmission events (34.3%) were for infection at the surgical site.

CONCLUSIONS

Longer length of hospital stay or admission to the intensive care unit significantly increased the likelihood of thirty-day readmission, regardless of demographics or discharge disposition. Marital status, Medicaid insurance status, and race may indicate how a patient's social and economic resources can impact his or her risk of being readmitted to the hospital.

LEVEL OF EVIDENCE

Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.

Facilitators and challenges to conducting interdisciplinary research

BACKGROUND

Complex, interconnected issues challenge the United States health care system and the patients and families it serves. System fragmentation, limited resources, rigid disciplinary boundaries, institutional culture, ineffective communication, and uncertainty surrounding health policy legislation are contributing to suboptimal care delivery and patient outcomes.

METHODS

These problems are too complex to be solved by a single discipline. Interdisciplinary research affords the opportunity to examine and solve some of these problems from a more integrative perspective using innovative and rigorous methodological designs.

RESULTS

In this paper, we explore lessons learned from exemplars funded by the Robert Wood Johnson Foundation's Interdisciplinary Nursing Quality Research Initiative.

DISCUSSION

The discussion is framed using an adaptation of the Interdisciplinary Research Model to evaluate improvements in individual health outcomes, health systems, and health policy. Barriers and facilitators to designing, conducting, and translating interdisciplinary research are discussed. Implications for health system and policy changes, including the need to provide funding mechanisms to implement interdisciplinary processes in both research and clinical practice, are provided.

Interventions for reducing readmissions - are we barking up the right tree?

Readmission reduction is at the focus of health care systems worldwide in efforts to improve efficiency across care settings. Yet, setting targets for readmission reduction is complicated due to inconsistencies in evidence pointing to effective organization-wide interventions and because of inverse incentives (such as maintaining high occupancy rates). Nonetheless, readmission reduction is one of the few quality measures that, if implemented properly, can serve as a catalyst for system integration. Appropriate mechanisms should be applied to hospitals as well as ambulatory settings to ensure that accountability is assigned to all stakeholders.

Association between elder self-neglect and hospice utilization in a community population

Elder self-neglect is associated with substantial 1-year mortality. However, hospice utilization among those with self-neglect remain unclear. The objective of this study is to quantify the prospective relation between self-neglect and risk for hospice utilization in a community population of older adults. Prospective population-based study in a geographically defined community in Chicago of older adults who participated in the Chicago Health and Aging Project. Of the 8669 participants in the Chicago Health and Aging Project, a subset of 1438 participants was reported to social services agency for suspected elder self-neglect. Outcome of interest was the hospice utilization obtained from the Center for Medicare and Medicaid System. Cox proportional hazard models were used to assess independent association of self-neglect with risk of hospice utilization using time-varying covariate analyses. After adjusting for potential confounding factors, elders who self-neglect was associated with increased risk for hospice utilization (HR, 2.43, 95% CI, 2.10-2.81). Greater self-neglect severity (mild: (HR, 2.12 (1.61-2.79); moderate: (HR, 2.36 (1.95-2.84); severe: (HR, 4.66 (2.98-7.30)) were associated with increased risk for hospice utilization. Interaction term analyses suggest that the significant relationship between self-neglect and hospice utilization was not mediated through medical conditions, cognitive impairment and physical disability. Moreover, self-neglect was associated with shorter length of stay in hospice (PE, -0.27, SE, 0.12, p<0.02) and shorter time from hospice admission to death (PE, -0.32, SE, 0.13, p<0.01). Elder self-neglect was associated with increased risk of hospice use in this community population. Elder self-neglect is associated with shorter length of stay in hospice care and shorter time from hospice admission to death.

Causes and implications of readmission after abdominal aortic aneurysm repair

OBJECTIVE

To determine the frequency, causes, predictors, and consequences of 30-day readmission after abdominal aortic aneurysm (AAA) repair.

BACKGROUND DATA

Centers for Medicare & Medicaid Services (CMS) will soon reduce total Medicare reimbursements for hospitals with higher-than-predicted 30-day readmission rates after vascular surgical procedures, including AAA repair. However, causes and factors leading to readmission in this population have never before been systematically analyzed.

METHODS

We analyzed elective AAA repairs over a 2-year period from the CMS Chronic Conditions Warehouse, a 5% national sample of Medicare beneficiaries.

RESULTS

A total of 2481 patients underwent AAA repair--1502 endovascular aneurysm repair (EVAR) and 979 open aneurysm repair. Thirty-day readmission rates were equivalent for EVAR (13.3%) and open repair (12.8%). Although wound complication was the most common reason for readmission after both procedures, the relative frequency of other causes differed-eg, bowel obstruction was common after open repair, and graft complication after EVAR. In multivariate analyses, preoperative comorbidities had a modest effect on readmission; however, postoperative factors, including serious complications leading to prolonged length of stay and discharge destination other than home, had a profound influence on the probability of readmission. The 1-year mortality in readmitted patients was 23.4% versus 4.5% in those not readmitted (P < 0.001).

CONCLUSIONS

Early readmission is common after AAA repair. Adjusting for comorbidities, postoperative events predict readmission, suggesting that proactively preventing, detecting, and managing postoperative complications may provide an approach to decreasing readmissions, with the potential to reduce cost and possibly enhance long-term survival.

[Orientation of patients referred by their general practionner to the public or private hospital sector in France: A prospective epidemiologic study]

PURPOSE

In-patients characteristics generate cost differences between hospitals. In France, there are few data on the characteristics on the patients referred to hospitals by their general practitioners (GPs) and none on the predictors of referral to the public or for-profit hospitals. The aim of this study was to analyze those characteristics and the predictors of referral to the public or for-profit hospitals.

METHODS

We collected, prospectively, the request for hospitalizations made by the GPs of the Sentinelles network in France, from 2007 to 2009. Patients' characteristics and also the reasons for that request were analyzed. A logistic regression was used to compare the population between local hospitals.

RESULTS

Ten thousand seven hundred and eighteen statements were collected. The median age was 73 years. Patients were women in 51% of the cases, and only 14% of the hospitalizations had been planned. Hospitalization in the public sector was preferred for young children and the elderly (P<0.001). When compared to the patients referred to the private sector, patients addressed to the public sector were more often seen for emergencies (OR: 2.3 [2.0-2.8]), by a doctor different from their referring GP (OR: 1.7 [1.4-2.1]) and out of the GP's office. The reasons for hospital admission were different depending on the sector of hospitalization (P<0.001), patients addressed to the public sector hospitals presented with greater comorbidity or more complex diagnosis (for example: feeling ill, fainting or syncope and fever) or a greater disability (for example: stroke, neurological and psychiatric diseases).

CONCLUSION

This study suggests that GPs send their patients to the public or for-profit hospitals according to criteria of severity, comorbidity and disability.

Department of Veterans Affairs Geriatric Research, Education and Clinical Centers: translating aging research into clinical geriatrics

Department of Veterans Affairs (VA) Geriatric Research, Education and Clinical Centers (GRECCs) originated in 1975 in response to the rapidly aging veteran population. Since its inception, the GRECC program has made major contributions to the advancement of aging research, geriatric training, and clinical care within and outside the VA. GRECCs were created to conduct translational research to enhance the clinical care of future aging generations. GRECC training programs also provide leadership in educating healthcare providers about the special needs of older persons. GRECC programs are also instrumental in establishing robust clinical geriatric and aging research programs at their affiliated university schools of medicine. This report identifies how the GRECC program has successfully adapted to changes that have occurred in VA since 1994, when the program's influence on U.S. geriatrics was last reported, focusing on its effect on advancing clinical geriatrics in the last 10 years. This evidence supports the conclusion that, after more than 30 years, the GRECC program remains a vibrant "jewel in the crown of the VA" and is poised to make contributions to aging research and clinical geriatrics well into the future.

Superior outcomes for rural patients after abdominal aortic aneurysm repair supports a systematic regional approach to abdominal aortic aneurysm care

OBJECTIVE

The impact of geographic isolation on abdominal aortic aneurysm (AAA) care in the United States is unknown. It has been postulated but not proven that rural patients have less access to endovascular aneurysm repair (EVAR), vascular surgeons, and high-volume treatment centers than their urban counterparts, resulting in inferior AAA care. The purpose of this study was to compare the national experience for treatment of intact AAA for patients living in rural areas or towns with those living in urban areas.

METHODS

Patients who underwent intact AAA repair in 2005 to 2006 were identified from a standard 5% random sample of all Medicare beneficiaries. Data on patient demographics, comorbidities, type of repair, and specialty of operating surgeon were collected. Hospitals were stratified into quintiles by yearly AAA volume. Primary outcomes included 30-day mortality and rehospitalization.

RESULTS

A total of 2616 patients had repair for intact AAA (40% open, 60% EVAR). Patients from rural and urban areas were equally likely to receive EVAR (rural 60% vs urban 61%; P = .99) and be treated by a vascular surgeon (rural 48% vs urban 50%; P = .82). Most rural patients (86%) received care in urban centers. Primary outcomes occurred in 11.6% of rural patients (1.3% 30-day mortality; 10.3% rehospitalization) vs 16.0% of urban patients (3% 30-day mortality, 13% rehospitalization; P = .04). In multivariate analyses, rural residence was independently associated with treatment at high-volume centers (odds ratio, 1.64; 95% confidence interval, 1.34-2.01; P < .0001) and decreased death or rehospitalization (odds ratio, 0.69; 95% confidence interval, 0.49-0.97; P = .03).

CONCLUSIONS

Despite geographic isolation, patients in rural areas needing treatment for intact AAAs have equivalent access to EVAR and vascular surgeons, increased referral to high-volume hospitals, and improved outcomes after repair. This suggests that urban patients may be disadvantaged even with nearby access to high-quality centers. This study supports the need for criteria that define centers of excellence to extend the benefit of regionalization to all patients.