Article

The Role of Outpatient Facilities in Explaining Variations in Risk-Adjusted Readmission Rates between Hospitals

Department of Pediatrics, Center for Outcomes Research, The Children's Hospital of Philadelphia, The University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
Health Services Research (Impact Factor: 2.49). 09/2009; 45(1):24-41. DOI: 10.1111/j.1475-6773.2009.01043.x
Source: PubMed

ABSTRACT Validate risk-adjusted readmission rates as a measure of inpatient quality of care after accounting for outpatient facilities, using premature infants as a test case.
Surviving infants born between January 1, 1998 and December 12, 2001 at five Northern California Kaiser Permanente neonatal intensive care units (NICU) with 1-year follow-up at 32 outpatient facilities.
Using a retrospective cohort of premature infants (N=898), Poisson's regression models determined the risk-adjusted variation in unplanned readmissions between 0-1 month, 0-3 months, 3-6 months, and 3-12 months after discharge attributable to patient factors, NICUs, and outpatient facilities.
Prospectively collected maternal and infant hospital data were linked to inpatient, outpatient, and pharmacy databases.
Medical and sociodemographic factors explained the largest amount of variation in risk-adjusted readmission rates. NICU facilities were significantly associated with readmission rates up to 1 year after discharge, but the outpatient facility where patients received outpatient care can explain much of this variation. Characteristics of outpatient facilities, not the NICUs, were associated with variations in readmission rates.
Ignoring outpatient facilities leads to an overstatement of the effect of NICUs on readmissions and ignores a significant cause of variations in readmissions.

0 Followers
 · 
88 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Good quality indicators should have face validity, relevance to patients, and be able to be measured reliably. Beyond these general requirements, good quality indicators should also have certain statistical properties, including sufficient variability to identify poor performers, relative insensitivity to severity adjustment, and the ability to capture what providers do rather than patients' characteristics. We assessed the performance of candidate indicators of ICU quality on these criteria. Indicators included ICU readmission, mortality, several length of stay outcomes, and the processes of venous-thromboembolism and stress ulcer prophylaxis provision. Retrospective cohort study SETTING:: One hundred thirty-eight U.S. ICUs from 2001-2008 in the Project IMPACT database. Two hundred sixty-eight thousand eight hundred twenty-four patients discharged from U.S. ICUs. None. We assessed indicators' (1) variability across ICU-years; (2) degree of influence by patient vs. ICU and hospital characteristics using the Omega statistic; (3) sensitivity to severity adjustment by comparing the area under the receiver operating characteristic curve (AUC) between models including vs. excluding patient variables, and (4) correlation between risk adjusted quality indicators using a Spearman correlation. Large ranges of among-ICU variability were noted for all quality indicators, particularly for prolonged length of stay (4.7-71.3%) and the proportion of patients discharged home (30.6-82.0%), and ICU and hospital characteristics outweighed patient characteristics for stress ulcer prophylaxis (ω, 0.43; 95% CI, 0.34-0.54), venous thromboembolism prophylaxis (ω, 0.57; 95% CI, 0.53-0.61), and ICU readmissions (ω, 0.69; 95% CI, 0.52-0.90). Mortality measures were the most sensitive to severity adjustment (area under the receiver operating characteristic curve % difference, 29.6%); process measures were the least sensitive (area under the receiver operating characteristic curve % differences: venous thromboembolism prophylaxis, 3.4%; stress ulcer prophylaxis, 2.1%). None of the 10 indicators was clearly and consistently correlated with a majority of the other nine indicators. No indicator performed optimally across assessments. Future research should seek to define and operationalize quality in a way that is relevant to both patients and providers.
    Critical care medicine 04/2014; 42(8). DOI:10.1097/CCM.0000000000000334 · 6.15 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The Pediatric Quality Measures Program is developing readmission measures for pediatric use. We sought to describe the importance of readmissions in children and the challenges of developing readmission quality measures. We consider findings and perspectives from research studies and commentaries in the pediatric and adult literature, characterizing arguments for and against using readmission rates as measures of pediatric quality and discussing available evidence and current knowledge gaps. The major topic of debate regarding readmission rates as pediatric quality measures is the relative influence of hospital quality versus other factors within and outside of health systems on readmission risk. The complex causation of readmissions leads to disagreement, particularly when rates are publicly reported or tied to payment, about whether readmissions can be prevented and how to achieve fair comparisons of readmission performance. Despite these controversies, the policy focus on readmissions has motivated widespread efforts by hospitals and outpatient providers to evaluate and reengineer care processes. Many adult studies demonstrate a link between successful initiatives to improve quality and reductions in readmissions. More research is needed on methods to enhance adjustment of readmission rates and on how to prevent pediatric readmissions.
    Academic Pediatrics 09/2014; 14(5):S39–S46. DOI:10.1016/j.acap.2014.06.012 · 2.23 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Objective To examine the viability of a hospital readmission quality metric for infants requiring neonatal intensive care. Methods Two cohorts were constructed. First, a cohort was constructed from infants born in California from 1995 to 2009 at 23 to 34 weeks' gestation, using birth certificates linked to maternal and infant inpatient records (N = 343,625). Second, the Medicaid Analytic eXtract (MAX) identified Medicaid-enrolled infants admitted to the neonatal intensive care unit (NICU) during their birth hospitalization in 18 states during 2006 to 2008 (N = 254,722). Hospital and state-level unadjusted readmission rates and rates adjusted for gestational age, birth weight, insurance status, gender, and common complications of preterm birth were calculated. Results Within California, there were wide variations in hospital-level readmission rates that were not completely explained through risk adjustment. Similar unadjusted variation was seen between states using MAX data, but risk adjustment and calculation of hospital-level rates were not possible because of missing gestational age, birth weight, and birth hospital data. Conclusions The California cohort shows significant variation in hospital-level readmission rates after risk adjustment, supporting the premise that readmission rates of prematurely born infants may reflect care quality. However, state data do not include term and early term infants requiring neonatal intensive care. MAX allows for multistate comparisons of all infants requiring NICU care. However, there were extensive missing data in the few states with sufficient information on managed care patients to calculate state-level measures. Constructing a valid readmission measure for NICU care across diverse states and regions requires improved data collection, including potential linkage between MAX data and vital statistics records.
    Academic Pediatrics 09/2014; 14(5):S47–S53. DOI:10.1016/j.acap.2014.06.010 · 2.23 Impact Factor

Full-text (2 Sources)

Download
35 Downloads
Available from
May 20, 2014