Effectiveness and cost of a transitional care program for heart failure: a prospective study with concurrent controls.

BACKGROUND: Randomized controlled trials have demonstrated the efficacy of nurse-led transitional care programs to reduce readmission rates for patients with heart failure; the effectiveness of these programs in real-world health care systems is less well understood. METHODS: We performed a prospective study with concurrent controls to test an advanced practice nurse-led transitional care program for patients with heart failure who were 65 years or older and were discharged from Baylor Medical Center Garland (BMCG) from August 24, 2009, through April 30, 2010. We compared the effect of the program on 30-day (from discharge) all-cause readmission rate, length of stay, and 60-day (from admission) direct cost for BMCG with that of other hospitals within the Baylor Health Care System. We also performed a budget impact analysis using costs and reimbursement experience from the intervention. RESULTS: The intervention significantly reduced adjusted 30-day readmission rates to BMCG by 48% during the postintervention period, which was better than the secular reductions seen at all other facilities in the system. The intervention had little effect on length of stay or total 60-day direct costs for BMCG. Under the current payment system, the intervention reduced the hospital financial contribution margin on average $227 for each Medicare patient with heart failure. CONCLUSIONS: Preliminary results suggest that transitional care programs reduce 30-day readmission rates for patients with heart failure. This underscores the potential of the intervention to be effective in a real-world setting, but payment reform may be required for the intervention to be financially sustainable by hospitals.

Impact of a standardized heart failure order set on mortality, readmission, and quality and costs of care.

OBJECTIVE: To determine the impact of a standardized heart failure order set on mortality, readmission, and quality and costs of care. DESIGN: Observational study. SETTING: Eight acute care hospitals and two specialty heart hospitals. PARTICIPANTS: All adults (>18 years) discharged from one of the included hospitals between December 2007 and March 2009 with a diagnosis of heart failure, who had not undergone heart transplant, did not have a left ventricular assistive device, and with a length of stay of 120 or less days. INTERVENTIONS: A standardized heart failure order set was developed internally, with content driven by the prevailing American College of Cardiology/American Heart Association clinical practice guidelines, and deployed systemwide via an intranet physician portal. MAIN OUTCOME MEASURES: Publicly reported process of care measures, in-patient mortality, 30-day mortality, 30-day readmission, length of stay, and direct cost of care were compared for heart failure patients treated with and without the order set. RESULTS: Order set used reached 73.1% in March 2009. After propensity score adjustment, order set use was associated with significantly increased core measures compliance [odds ratio (95% confidence interval) = 1.51(1.08; 2.12)] and reduced in-patient mortality [odds ratio (95% confidence interval) = 0.49(0.28; 0.88)]. Reductions in 30-day mortality and readmission approached significance. Direct cost for initial admissions alone and in combination with readmissions were significantly lower with order set use. CONCLUSIONS: Implementing an evidence-based standardized order set may help improve outcomes, reduce costs of care and increase adherence to evidence-based processes of care.

Statistical models and patient predictors of readmission for acute myocardial infarction: a systematic review.

BACKGROUND: Readmission after acute myocardial infarction (AMI) has been targeted for public reporting because it is a common, costly, and often preventable outcome. To assist in ongoing efforts to risk-stratify patients and profile hospitals through public reporting of performance measures, we conducted a systematic review to identify models designed to compare hospital rates of readmission or predict patients' risk of readmission after AMI and to identify studies evaluating patient characteristics associated with AMI readmission. METHODS AND RESULTS: We identified relevant English-language studies published between 1950 and 2007 by searching MEDLINE, Scopus, PsycINFO, and all 4 Ovid Evidence-Based Medicine Reviews. Eligible publications reported on readmission up to 1 year after AMI hospitalization among adults. From 751 potentially relevant articles, 35 met our predefined inclusion/exclusion criteria. Overall, none developed models to compare readmission rates among hospitals or models to predict patients' risk of readmission. All 35 examined patient characteristics associated with AMI readmission. However, studies varied in methods for case and outcome identification, used multiple types of data sources, examined differing outcomes (often either readmission alone or a composite outcome of readmission or death) over varying follow-up periods (from 30 days to 1 year), and found few patient characteristics consistently associated with readmission. CONCLUSIONS: Patient characteristics may be important predictors of AMI readmission; however, few variables were consistently identified. Thus, clinically, patient risk stratification is challenging. From a policy perspective, a validated risk-standardized model to profile hospitals using AMI readmission rates is currently unavailable in the literature.

Comparing the financial condition of Texas hospitals using a novel definition for the safety net.

Lack of health insurance is more prevalent in the state of Texas than in the rest of the country. To get necessary medical care, uninsured Texans must rely on safety net hospitals. Economic turmoil and fluctuating public support routinely threaten the financial stability of these hospitals. Safety net hospitals must be identified to craft public policy solutions that ensure their viability. In this paper, we propose a new method to identify these hospitals by incorporating criteria established previously by economists with additional measures of community value. Our data indicate that safety net hospitals continue to face financial challenges. Texas will need to move forward along several policy fronts to preserve this vital system of care.

An administrative claims measure suitable for profiling hospital performance on the basis of 30-day all-cause readmission rates among patients with heart failure.

BACKGROUND: Readmission soon after hospital discharge is an expensive and often preventable event for patients with heart failure. We present a model approved by the National Quality Forum for the purpose of public reporting of hospital-level readmission rates by the Centers for Medicare & Medicaid Services. METHODS AND RESULTS: We developed a hierarchical logistic regression model to calculate hospital risk-standardized 30-day all-cause readmission rates for patients hospitalized with heart failure. The model was derived with the use of Medicare claims data for a 2004 cohort and validated with the use of claims and medical record data. The unadjusted readmission rate was 23.6%. The final model included 37 variables, had discrimination ranging from 15% observed 30-day readmission rate in the lowest predictive decile to 37% in the upper decile, and had a c statistic of 0.60. The 25th and 75th percentiles of the risk-standardized readmission rates across 4669 hospitals were 23.1% and 24.0%, with 5th and 95th percentiles of 22.2% and 25.1%, respectively. The odds of all-cause readmission for a hospital 1 standard deviation above average was 1.30 times that of a hospital 1 standard deviation below average. State-level adjusted readmission rates developed with the use of the claims model are similar to rates produced for the same cohort with the use of a medical record model (correlation, 0.97; median difference, 0.06 percentage points). CONCLUSIONS: This claims-based model of hospital risk-standardized readmission rates for heart failure patients produces estimates that may serve as surrogates for those derived from a medical record model.