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Items: 1 to 20 of 81

1.

Challenges and benefits of adding laboratory data to a mortality risk adjustment method.

McCullough E, Sullivan C, Banning P, Goldfield N, Hughes J.

Qual Manag Health Care. 2011 Oct-Dec;20(4):253-62. doi: 10.1097/QMH.0b013e318231cf4f.

PMID:
21971023
3.

Modifying ICD-9-CM coding of secondary diagnoses to improve risk-adjustment of inpatient mortality rates.

Pine M, Jordan HS, Elixhauser A, Fry DE, Hoaglin DC, Jones B, Meimban R, Warner D, Gonzales J.

Med Decis Making. 2009 Jan-Feb;29(1):69-81. doi: 10.1177/0272989X08323297. Epub 2008 Sep 23.

PMID:
18812585
4.

Enhancement of claims data to improve risk adjustment of hospital mortality.

Pine M, Jordan HS, Elixhauser A, Fry DE, Hoaglin DC, Jones B, Meimban R, Warner D, Gonzales J.

JAMA. 2007 Jan 3;297(1):71-6.

PMID:
17200477
5.

The measurement and monitoring of surgical adverse events.

Bruce J, Russell EM, Mollison J, Krukowski ZH.

Health Technol Assess. 2001;5(22):1-194. Review.

6.

Using Medicare claims data to assess provider quality for CABG surgery: does it work well enough?

Hannan EL, Racz MJ, Jollis JG, Peterson ED.

Health Serv Res. 1997 Feb;31(6):659-78.

7.

Evaluation of the HCFA model for the analysis of mortality following hospitalization.

Krakauer H, Bailey RC, Skellan KJ, Stewart JD, Hartz AJ, Kuhn EM, Rimm AA.

Health Serv Res. 1992 Aug;27(3):317-35.

8.

An administrative claims model suitable for profiling hospital performance based on 30-day mortality rates among patients with heart failure.

Krumholz HM, Wang Y, Mattera JA, Wang Y, Han LF, Ingber MJ, Roman S, Normand SL.

Circulation. 2006 Apr 4;113(13):1693-701. Epub 2006 Mar 20.

9.

Severity of illness measures derived from the Uniform Clinical Data Set (UCDSS).

Hartz AJ, Guse C, Sigmann P, Krakauer H, Goldman RS, Hagen TC.

Med Care. 1994 Sep;32(9):881-901.

PMID:
8090042
10.

An administrative claims model suitable for profiling hospital performance based on 30-day mortality rates among patients with an acute myocardial infarction.

Krumholz HM, Wang Y, Mattera JA, Wang Y, Han LF, Ingber MJ, Roman S, Normand SL.

Circulation. 2006 Apr 4;113(13):1683-92. Epub 2006 Mar 20.

11.

Clinical redesign using all patient refined diagnosis related groups.

Sedman AB, Bahl V, Bunting E, Bandy K, Jones S, Nasr SZ, Schulz K, Campbell DA.

Pediatrics. 2004 Oct;114(4):965-9.

PMID:
15466092
12.

Predictions of hospital mortality rates: a comparison of data sources.

Pine M, Norusis M, Jones B, Rosenthal GE.

Ann Intern Med. 1997 Mar 1;126(5):347-54.

PMID:
9054278
13.

Combining administrative and clinical data to stratify surgical risk.

Fry DE, Pine M, Jordan HS, Elixhauser A, Hoaglin DC, Jones B, Warner D, Meimban R.

Ann Surg. 2007 Nov;246(5):875-85.

PMID:
17968182
14.

The hazards of using administrative data to measure surgical quality.

Fry DE, Pine MB, Jordan HS, Hoaglin DC, Jones B, Meimban R.

Am Surg. 2006 Nov;72(11):1031-7; discussion 1061-9, 1133-48. Erratum in: Am Surg. 2007 Feb;73(2):199.

PMID:
17120944
15.

Mortality after cardiac bypass surgery: prediction from administrative versus clinical data.

Geraci JM, Johnson ML, Gordon HS, Petersen NJ, Shroyer AL, Grover FL, Wray NP.

Med Care. 2005 Feb;43(2):149-58.

PMID:
15655428
16.

Diagnostic, pharmacy-based, and self-reported health measures in risk equalization models.

Stam PJ, van Vliet RC, van de Ven WP.

Med Care. 2010 May;48(5):448-57. doi: 10.1097/MLR.0b013e3181d559b4.

PMID:
20393368
17.
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19.

Administrative versus clinical data for coronary artery bypass graft surgery report cards: the view from California.

Parker JP, Li Z, Damberg CL, Danielsen B, Carlisle DM.

Med Care. 2006 Jul;44(7):687-95.

PMID:
16799364
20.

Length of stay predictions: improvements through the use of automated laboratory and comorbidity variables.

Liu V, Kipnis P, Gould MK, Escobar GJ.

Med Care. 2010 Aug;48(8):739-44. doi: 10.1097/MLR.0b013e3181e359f3.

PMID:
20613662

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