Literature DB >> 8048435

Facility mortality rates for new end-stage renal disease patients: implications for quality improvement.

W McClellan1, J M Soucie.   

Abstract

End-stage renal disease networks can provide clinicians with valuable information about treatment outcome among their patients compared with those of other providers. These comparisons can help clinicians identify potential quality of care problems and efficiently allocate resources for quality improvement. We have illustrated this application of network information by examining the mortality rates for newly treated end-stage renal disease patients in 161 dialysis facilities in North Carolina, South Carolina, and Georgia. We found that mortality rates were high (an average of 19.2 deaths per 100 years of treatment) and variable (ranging from 0 to 43 deaths per 100 dialysis years). The risk of a patient dying in a facility at the 75th percentile of mortality was 50% higher than that of a patient in a facility at the 25th percentile. Adjusting for patient characteristics (case mix) left considerable variation in the risk of dying among individual dialysis facilities unexplained, suggesting that other treatment center-specific aspects of care contributed to the differences in mortality. After controlling for factors associated with increased mortality, the risk of a patient dying in a facility at the 75th percentile of mortality was 70% greater than that of a patient in a facility at the 25th percentile of mortality. Most facilities, but not all, with the highest unadjusted mortality rates also had the highest adjusted mortality. We conclude that treatment outcome comparisons that have been adjusted to account for case mix among facilities can be provided by network surveillance systems and, when properly understood by providers, might stimulate the search for facility-specific, nonpatient factors that contribute to these outcomes.

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Year:  1994        PMID: 8048435     DOI: 10.1016/s0272-6386(12)80193-4

Source DB:  PubMed          Journal:  Am J Kidney Dis        ISSN: 0272-6386            Impact factor:   8.860


  3 in total

1.  How much better can we predict dialysis patient survival using clinical data?

Authors:  D E Mesler; S Byrne-Logan; E P McCarthy; A S Ash; M A Moskowitz
Journal:  Health Serv Res       Date:  1999-04       Impact factor: 3.402

2.  Warfarin use associates with increased risk for stroke in hemodialysis patients with atrial fibrillation.

Authors:  Kevin E Chan; J Michael Lazarus; Ravi Thadhani; Raymond M Hakim
Journal:  J Am Soc Nephrol       Date:  2009-08-27       Impact factor: 10.121

3.  A data-driven approach to improving the care of in-center hemodialysis patients.

Authors:  W M McClellan; P R Frederick; S D Helgerson; R P Hayes; D J Ballard; M McMullan
Journal:  Health Care Financ Rev       Date:  1995
  3 in total

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