Dataset Information

Assessing the impact of nurse post-discharge telephone calls on 30-day hospital readmission rates.

ABSTRACT:

Background

Several care transition interventions propose that post-discharge phone calls can reduce adverse events and decrease costly return visits to the hospital. However, given the multi-faceted nature of most care transitions interventions, the true relationship between post-discharge phone calls and readmissions in a real world setting is uncertain.

Objective

To determine the effect of receiving a post-discharge telephone call on all-cause 30-day readmission in a general medicine population.

Design

Retrospective observational study.

Participants

Patients discharged home from the Medicine Service at a tertiary care academic medical center between November 2010 and May 2012.

Intervention

Patients received two telephone call attempts by a nurse within 72 h of discharge. Nurses followed a standard script to address issues associated with readmission.

Main outcome and measures

Billing data captured readmissions. We used logistic regression-adjusted patient and clinical covariates as well as a propensity score representing likelihood of being called to determine the association between call receipt and risk for readmission.

Key results

There were 5,507 eligible patients. In unadjusted analyses, patients who received a call and completed the intervention were significantly less likely to be readmitted compared to those who did not [155 (5.8 %) vs 123 (8.6 %), p < 0.01]. In multivariable models adjusting for socio-demographic and clinical covariates alone, completing a post-discharge telephone call intervention was associated with lower odds for readmission (AOR 0.71; 95 % CI: 0.55-0.91). However, when models adjusted for the likelihood of receiving the phone call using the propensity score, no association between call receipt and readmission was observed (AOR 0.91; 95%CI: 0.69-1.20).

Conclusions

Effectiveness of post-discharge phone call programs may be more related to whether patients are able to answer a phone call than to the care delivered by the phone call. Programs would benefit from improving their ability to perform phone outreach while simultaneously improving on the care delivered during the calls.

SUBMITTER: Harrison JD

PROVIDER: S-EPMC4238208 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Assessing the impact of nurse post-discharge telephone calls on 30-day hospital readmission rates.

Harrison James D JD Auerbach Andrew D AD Quinn Kathryn K Kynoch Ellen E Mourad Michelle M

Journal of general internal medicine 20141101 11

<h4>Background</h4>Several care transition interventions propose that post-discharge phone calls can reduce adverse events and decrease costly return visits to the hospital. However, given the multi-faceted nature of most care transitions interventions, the true relationship between post-discharge phone calls and readmissions in a real world setting is uncertain.<h4>Objective</h4>To determine the effect of receiving a post-discharge telephone call on all-cause 30-day readmission in a general med ...[more]

PMID: 25103122

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Project description:BackgroundLowering hospital readmission rates has become a primary target for the Centers for Medicare & Medicaid Services, but studies of the relationship between adherence to the recommended hospital care processes and readmission rates have provided inconsistent and inconclusive results.ObjectiveTo examine the association between hospital performance on Medicare's Hospital Compare process quality measures and 30-day readmission rates for patients with acute myocardial infarction (AMI), heart failure and pneumonia, and for those undergoing major surgery.Design, setting and participantsWe assessed hospital performance on process measures using the 2007 Hospital Inpatient Quality Reporting Program. The process measures for each condition were aggregated in two separate measures: Overall Measure (OM) and Appropriate Care Measure (ACM) scores. Readmission rates were calculated using Medicare claims.Main outcome measureRisk-standardized 30-day all-cause readmission rate was calculated as the ratio of predicted to expected rate standardized by the overall mean readmission rate. We calculated predicted readmission rate using hierarchical generalized linear models and adjusting for patient-level factors.ResultsAmong patients aged ≥ 66 years, the median OM score ranged from 79.4 % for abdominal surgery to 95.7 % for AMI, and the median ACM scores ranged from 45.8 % for abdominal surgery to 87.9 % for AMI. We observed a statistically significant, but weak, correlation between performance scores and readmission rates for pneumonia (correlation coefficient R = 0.07), AMI (R = 0.10), and orthopedic surgery (R = 0.06). The difference in the mean readmission rate between hospitals in the 1st and 4th quartiles of process measure performance was statistically significant only for AMI (0.25 percentage points) and pneumonia (0.31 percentage points). Performance on process measures explained less than 1 % of hospital-level variation in readmission rates.ConclusionsHospitals with greater adherence to recommended care processes did not achieve meaningfully better 30-day hospital readmission rates compared to those with lower levels of performance.

Project description:BackgroundRestrictive transfusion strategies supported by large randomized trials are resulting in decreased blood utilization in cardiac surgery. What remains to be determined, however, is the impact of lower discharge hemoglobin (Hb) levels on readmission rates. We assessed patients with higher versus lower Hb levels on discharge to compare 30-day readmission rates after coronary artery bypass grafting (CABG).MethodsWe retrospectively evaluated 1552 patients undergoing isolated CABG at our institution from January 2013 to May 2016. We evaluated 2 Hb cohorts: "high" (above) and "low" (below) the mean discharge Hb level of 9.4 g/dL, comparing patient characteristics, blood utilization, and clinical outcomes including 30-day readmission rates. We further evaluated the effects of the lowest (<8 g/dL) discharge Hb levels on 30-day readmission rates by dividing the patients into 4 anemia cohorts based on discharge Hb levels: "no anemia" (>12 g/dL), "mild anemia" (10-11.9 g/dL), "moderate anemia" (8-9.9 g/dL), and "severe anemia" (<8 g/dL). Risk adjustment accounted for age, sex, Charlson comorbidity index, preoperative comorbidities, revision sternotomy, and patient blood management program implementation.ResultsThe "high" and "low" groups had similar patient characteristics except for Hb levels (mean discharge Hb was 10.4 ± 0.9 vs 8.5 ± 0.6 g/dL, respectively). Notably, no evidence for a difference in 30-day readmission rates was noted between the "high" (76/746; 10.2%) and "low" (97/806; 12.0%) (P = .25) Hb cohorts. The 4 anemia cohorts had differences in age, revision sternotomy incidence, Hb levels, certain patient comorbidities, and time to readmission. On multivariable analysis, the risk-adjusted odds of readmission in the "low" Hb cohort (odds ratio, 1.16; 95% confidence interval, 0.84-1.61; P = .36) was not significant compared to the "high" Hb cohort. Compared to patients with discharge Hb ≥8 g/dL, patients with Hb <8 g/dL had a higher incidence of readmission (22/129; 17.1% vs 151/1423; 10.6%; P = .036). On multivariable analysis, Hb <8 g/dL on discharge was predictive of readmission (odds ratio, 1.77; 95% confidence interval, 1.05-2.88; P = .03). The most common reason for readmission was volume overload, followed by infection and arrhythmias.ConclusionsA discharge Hb level below the institution mean for CABG patients does not provide evidence for an association with an increased 30-day readmission rate. In the small number of patients discharged with Hb <8 g/dL, there is a suggestion of increased risk for readmission and larger more controlled studies are needed to verify or refute this finding.

Project description:ImportanceThirty-day readmission rates have been suggested as a marker for quality of care. By investigating the factors associated with readmissions in all otolaryngology subspecialties we provide data relevant for the development of risk stratification systems to improve outcomes.ObjectiveTo establish the association of surgical and hospital volume and patient characteristics with 30-day readmission rates to guide the development of otolaryngology-specific risk stratification models.Design, setting, and participantsA retrospective cohort study including adult patients who underwent inpatient otolaryngology surgery in New York State between 1995 and 2015 was conducted using the Statewide Planning and Research Cooperative System (SPARCS). Regression techniques were used to describe relationships of patient-level factors, hospital, and surgeon volume to 30-day readmission rates in New York State.Main outcomes and measuresThe main outcome measures were patient-, surgeon-, and hospital-level risk factors for readmission. Secondary outcome measures were rate of readmissions by subspecialty procedure and by diagnosis on readmission.ResultsWe identified 254 257 cases of otolaryngology surgery (147 065 women [58%], mean [SD] age 50 [17] years). The 30-day readmission rate was 6%. In a multivariable model, odds ratios (ORs) identified Medicaid insurance (OR, 1.46; 99% CI, 1.36-1.57), Medicare insurance (OR, 1.32; 99% CI, 1.24-1.42), bottom quartile income (OR, 1.08; 99% CI, 1.01-1.15), patient comorbidities measured by the Charlson Comorbidity Index (CCI) (CCI >1; OR, 2.31; 99% CI, 2.16-2.47), length of stay (LOS) (LOS >10 days; OR, 2.29; 99% CI, 2.00-2.45), rhinology (OR, 1.37; 99% CI, 1.24-1.51), laryngology (OR, 1.98; 99% CI, 1.62-2.43), and head and neck cancer (OR, 1.27; 99% CI, 1.17-1.37) procedures as readmission predictors. High-volume surgeons were protective of 30-day readmission (OR, 0.67; 99% CI, 0.635-0.708) relative to low volume. Hospital volume was not significantly associated to readmissions. The most common causes of readmission included wound- (2682 patients, 18%), respiratory- (1776 patients, 12%), cardiovascular- (1210 patients, 8%), and volume- (1089 patients, 7%) related disorders.Conclusions and relevanceThis study evaluated the combined effects of patient-, surgeon-, and hospital-level factors on 30-day readmission after otolaryngology surgery. Socioeconomic factors, patient comorbidities, surgeon volumes, and procedure were significantly associated with 30-day readmission. Though the cause of 30-day readmission is multifactorial, a large portion is driven by socioeconomic factors. Addressing these disparities at the system level is necessary to address the described readmission disparities. The development of risk-stratification models based on patient-, procedure-, and surgeon-level factors may help facilitate resource distribution.

Project description:ObjectivesThe US government implemented the Hospital Readmission Reduction Program on 1 October 2012 to reduce readmission rates through financial penalties to hospitals with excessive readmissions. We conducted a pooled cross-sectional analysis of US hospitals from 2009 to 2015 to determine the association of the Hospital Readmission Reduction Program with 30-day readmissions.MethodsWe utilized multivariable linear regression with year and state fixed effects. The model was adjusted for hospital and market characteristics lagged by 1 year. Interaction effects of hospital and market characteristics with the Hospital Readmission Reduction Program indicator variable was also included to assess whether associations of Hospital Readmission Reduction Program with 30-day readmissions differed by these characteristics.ResultsIn multivariable adjusted analysis, the main effect of the Hospital Readmission Reduction Program was a 3.80 percentage point (p < 0.001) decrease in readmission rates in 2013-2015 relative to 2009-2012. Hospitals with lower readmission rates overall included not-for-profit and government hospitals, medium and large hospitals, those in markets with a larger percentage of Hispanic residents, and population 65 years and older. Higher hospital readmission rates were observed among those with higher licensed practical nurse staffing ratio, larger Medicare and Medicaid share, and less competition. Statistically significant interaction effects between hospital/market characteristics and the Hospital Readmission Reduction Program on the outcome of 30-day readmission rates were present. Teaching hospitals, rural hospitals, and hospitals in markets with a higher percentage of residents who were Black experienced larger decreases in readmission rates. Hospitals with larger registered nurse staffing ratios and in markets with higher uninsured rate and percentage of residents with a high school education or greater experienced smaller decreases in readmission rates.ConclusionFindings of the current study support the effectiveness of the Hospital Readmission Reduction Program but also point to the need to consider the ability of hospitals to respond to penalties and incentives based on their characteristics during policy development.

Project description:ImportanceThe Affordable Care Act has led to US national reductions in hospital 30-day readmission rates for heart failure (HF), acute myocardial infarction (AMI), and pneumonia. Whether readmission reductions have had the unintended consequence of increasing mortality after hospitalization is unknown.ObjectiveTo examine the correlation of paired trends in hospital 30-day readmission rates and hospital 30-day mortality rates after discharge.Design, setting, and participantsRetrospective study of Medicare fee-for-service beneficiaries aged 65 years or older hospitalized with HF, AMI, or pneumonia from January 1, 2008, through December 31, 2014.ExposureThirty-day risk-adjusted readmission rate (RARR).Main outcomes and measuresThirty-day RARRs and 30-day risk-adjusted mortality rates (RAMRs) after discharge were calculated for each condition in each month at each hospital in 2008 through 2014. Monthly trends in each hospital's 30-day RARRs and 30-day RAMRs after discharge were examined for each condition. The weighted Pearson correlation coefficient was calculated for hospitals' paired monthly trends in 30-day RARRs and 30-day RAMRs after discharge for each condition.ResultsIn 2008 through 2014, 2 962 554 hospitalizations for HF, 1 229 939 for AMI, and 2 544 530 for pneumonia were identified at 5016, 4772, and 5057 hospitals, respectively. In January 2008, mean hospital 30-day RARRs and 30-day RAMRs after discharge were 24.6% and 8.4% for HF, 19.3% and 7.6% for AMI, and 18.3% and 8.5% for pneumonia. Hospital 30-day RARRs declined in the aggregate across hospitals from 2008 through 2014; monthly changes in RARRs were -0.053% (95% CI, -0.055% to -0.051%) for HF, -0.044% (95% CI, -0.047% to -0.041%) for AMI, and -0.033% (95% CI, -0.035% to -0.031%) for pneumonia. In contrast, monthly aggregate changes across hospitals in hospital 30-day RAMRs after discharge varied by condition: HF, 0.008% (95% CI, 0.007% to 0.010%); AMI, -0.003% (95% CI, -0.005% to -0.001%); and pneumonia, 0.001% (95% CI, -0.001% to 0.003%). However, correlation coefficients in hospitals' paired monthly changes in 30-day RARRs and 30-day RAMRs after discharge were weakly positive: HF, 0.066 (95% CI, 0.036 to 0.096); AMI, 0.067 (95% CI, 0.027 to 0.106); and pneumonia, 0.108 (95% CI, 0.079 to 0.137). Findings were similar in secondary analyses, including with alternate definitions of hospital mortality.Conclusions and relevanceAmong Medicare fee-for-service beneficiaries hospitalized for heart failure, acute myocardial infarction, or pneumonia, reductions in hospital 30-day readmission rates were weakly but significantly correlated with reductions in hospital 30-day mortality rates after discharge. These findings do not support increasing postdischarge mortality related to reducing hospital readmissions.

Dataset Information

Assessing the impact of nurse post-discharge telephone calls on 30-day hospital readmission rates.

Background

Objective

Design

Participants

Intervention

Main outcome and measures

Key results

Conclusions

Publications

Assessing the impact of nurse post-discharge telephone calls on 30-day hospital readmission rates.

Similar Datasets

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