Literature DB >> 9547683

Quality indicators using hospital discharge data: state and national applications.

M Johantgen1, A Elixhauser, J K Bali, M Goldfarb, D R Harris.   

Abstract

BACKGROUND: Demand for information about the quality of health care has escalated. Yet many organizations lack well-specified quality measures, statistical expertise, or the requisite data to produce such information. The Healthcare Cost and Utilization Project Quality Indicators (HCUP QIs) represent one approach to measuring health care quality using readily available data on hospital inpatients.
METHODS: The HCUP QIs, developed in 1994, address clinical performance rather than other dimensions of quality such as satisfaction or efficiency. The 33 indicators produce rates that represent measures of outcomes (mortality and complications), utilization, and access. In lieu of complex multivariate techniques, two methods were used: (1) restrictions in defining patient subgroups to isolate homogeneous at-risk populations and (2) standardization when populations are diverse. Stratified analyses are recommended when patient or hospital factors are believed to influence the outcome. A simple method for making statistical comparisons to national rates was developed. The HCUP QI software, available in both mainframe and microcomputer applications, have enabled organizations to use their own data to produce comparative statistics and examine trends over time. Results summarized at the individual hospital or aggregate level are being used to stimulate continuous quality improvement initiatives.
CONCLUSIONS: The HCUP QIs offer a low-cost alternative for organizations that have access to administrative data. Current users include hospital associations, state health departments, statewide data organizations, and individual hospitals. Although the HCUP QIs are intended to serve as indicators, not definitive measures, of quality, they were designed to highlight quality concerns and to target areas for more intensive study.

Entities:  

Mesh:

Year:  1998        PMID: 9547683     DOI: 10.1016/s1070-3241(16)30364-9

Source DB:  PubMed          Journal:  Jt Comm J Qual Improv        ISSN: 1070-3241


  11 in total

Review 1.  Administrative data based patient safety research: a critical review.

Authors:  C Zhan; M R Miller
Journal:  Qual Saf Health Care       Date:  2003-12

2.  How safe is primary knee replacement surgery? Perioperative complication rates in Northern Illinois, 1993-1999.

Authors:  Joe Feinglass; Hagay Amir; Patricia Taylor; Ithai Lurie; Larry M Manheim; Rowland W Chang
Journal:  Arthritis Rheum       Date:  2004-02-15

3.  Benchmarking physical therapy clinic performance: statistical methods to enhance internal validity when using observational data.

Authors:  Linda Resnik; Dawei Liu; Dennis L Hart; Vince Mor
Journal:  Phys Ther       Date:  2008-08-08

4.  Hospital Readmission among New Dialysis Patients Associated with Young Age and Poor Functional Status.

Authors:  LaTonya J Hickson; Bjorg Thorsteinsdottir; Priya Ramar; Megan S Reinalda; Cynthia S Crowson; Amy W Williams; Robert C Albright; Macaulay A Onuigbo; Andrew D Rule; Nilay D Shah
Journal:  Nephron       Date:  2018-01-09       Impact factor: 2.847

Review 5.  Development of quality indicators for care of chronic kidney disease in the primary care setting using electronic health data: a RAND-modified Delphi method.

Authors:  Shingo Fukuma; Sayaka Shimizu; Kakuya Niihata; Ken-Ei Sada; Motoko Yanagita; Tsuguru Hatta; Masaomi Nangaku; Ritsuko Katafuchi; Yoshiro Fujita; Junji Koizumi; Shunzo Koizumi; Kenjiro Kimura; Shunichi Fukuhara; Yugo Shibagaki
Journal:  Clin Exp Nephrol       Date:  2016-05-04       Impact factor: 2.801

6.  The risk of risk-adjustment measures for perioperative spine infection after spinal surgery.

Authors:  Adam P Goode; Chad Cook; J Brian Gill; Sean Tackett; Christopher Brown; William Richardson
Journal:  Spine (Phila Pa 1976)       Date:  2011-04-20       Impact factor: 3.468

7.  Nurse staffing and postsurgical adverse events: an analysis of administrative data from a sample of U.S. hospitals, 1990-1996.

Authors:  Christine Kovner; Cheryl Jones; Chunliu Zhan; Peter J Gergen; Jayasree Basu
Journal:  Health Serv Res       Date:  2002-06       Impact factor: 3.402

8.  Measuring hospital quality: can medicare data substitute for all-payer data?

Authors:  Jack Needleman; Peter I Buerhaus; Soeren Mattke; Maureen Stewart; Katya Zelevinsky
Journal:  Health Serv Res       Date:  2003-12       Impact factor: 3.402

9.  Hip fracture in hospitalized medical patients.

Authors:  Antonio Zapatero; Raquel Barba; Jesús Canora; Juan E Losa; Susana Plaza; Jesús San Roman; Javier Marco
Journal:  BMC Musculoskelet Disord       Date:  2013-01-08       Impact factor: 2.362

10.  Comparison of outcomes and utilization of extracranial-intracranial bypass versus intracranial stenting for intracranial stenosis.

Authors:  Taylor A Wilson; Omar Tanweer; Paul P Huang; Howard A Riina
Journal:  Surg Neurol Int       Date:  2014-12-11
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.