Discrepancy between admission and discharge diagnoses as a predictor of hospital length of stay

Journal of Hospital Medicine

Volumn 4, Issue 4, 2009, Pages 234-239

  Johnson, Tricia ^a   McNutt, Robert ^a,b   Odwazny, Richard ^a,b   Patel, Deval ^a   Baker, Seth ^b  

^a RUSH UNIVERSITY   (United States)

^b RUSH UNIVERSITY MEDICAL CENTER   (United States)

Author keywords

Administrative data; Diagnosis codes; Diagnosis discordance; Diagnostic uncertainty; Health services research; Hospital care; Length of stay

Indexed keywords

ADULT; ARTICLE; CLINICAL ASSESSMENT; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC ERROR; DIAGNOSTIC TEST; DISEASE CLASSIFICATION; FEMALE; HEALTH CARE QUALITY; HOSPITAL ADMISSION; HOSPITAL DISCHARGE; HOSPITAL PATIENT; HUMAN; LENGTH OF STAY; MAJOR CLINICAL STUDY; MALE; MEDICAL DOCUMENTATION; PATIENT ASSESSMENT; PRIORITY JOURNAL; PROGNOSIS; RETROSPECTIVE STUDY; RISK FACTOR;

ADULT; AGED; CHICAGO; FEMALE; HUMANS; LENGTH OF STAY; MALE; MEDICAID; MEDICARE; MIDDLE AGED; PATIENT ADMISSION; PATIENT DISCHARGE; PATIENTS' ROOMS; PREDICTIVE VALUE OF TESTS; REGRESSION ANALYSIS; UNITED STATES; YOUNG ADULT;

EID: 67649494731     PISSN: 15535592     EISSN: 15535606     Source Type: Journal    
DOI: 10.1002/jhm.453     Document Type: Article

References (10)

1. Pine M, Jordan HS, Elixhauser A, et al. Enhancement of claims data to improve risk adjustment of hospital mortality. JAMA. 2007;297:71-76.
2. Iezzoni LI. The risks of risk adjustment. JAMA. 1997; 278:1600-1607.
3. Jencks SF, Huff ED, Cuerdon T. Change in the quality of care delivered to Medicare beneficiaries, 1998-1999 to 2000-2001. JAMA. 2003;289: 305-312.
4. Graff LG, Wang Y, Borkowski B, et al. Delay in the diagnosis of acute myocardial infarction: Effect on quality of care and its assessment. Acad Emerg Med. 2006;13:931-938.
5. Rochon PA, Katz JN, Morrow LA, et al. Comorbid illness is associated with survival and length of hospital stay in patients with chronic disability. A prospective comparison of three comorbidity indices. Med Care. 1996;34:1093-1101.
6. Iezzoni LI, Foley SM, Daley J, Hughes J, Fisher ES, Heeren T. Comorbidities, complications and coding bias: does the number of diagnosis codes matter in predicting in-hospital mortality? JAMA. 1992;267:2197-2203.
7. Elixhauser A, Steiner C, Palmer L. Clinical classifications software (CCS), 2007. Available at: http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ ccs.jsp. Accessed December 2008.
8. Parker JP, McCombs JS, Graddy EA. Can pharmacy data improve prediction of hospital outcomes? Comparison with a diagnosis-based comorbidity measure. Med Care. 2003;41:407-419.
9. Melfi C, Holleman E, Arthur D, Katz B. Selecting a patient characteristics index for the prediction of medical outcomes using administrative data. J Clin Epidemiol. 1995;48:917-926.
10. Kieszak SM, Flanders WD, Kosinski AS, Shipp CC, Karp H. A comparison of the Charlson Comorbidity Index derived from medical record data and administrative billing data. J Clin Epidemiol. 1999;52:137-142.