Literature DB >> 19388065

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

Tricia Johnson1, Robert McNutt, Richard Odwazny, Deval Patel, Seth Baker.   

Abstract

CONTEXT: The addition of clinical data or present on admission (POA) codes to administrative databases improves the accuracy of predicting clinical outcomes, such as inpatient mortality. Other POA information may also explain variation in hospital outcomes, such as length of stay (LOS), but this potential has not been previously explored.
OBJECTIVES: To assess whether a discrepancy between the diagnosis coded at the time of admission and the diagnoses coded at discharge independently explains variation in LOS for general internal medicine patients. DESIGN, SETTING, AND PATIENTS: A retrospective data review of patients age 18 years and older admitted to general internal medicine units at a large, urban academic medical center between July 2005 and June 2006. A generalized linear regression model was constructed to adjust for patient factors known to be associated with LOS. OUTCOME MEASURE: Average LOS among patients with a discrepancy between the admitting and discharge diagnosis codes versus those patients without a discrepancy. MAIN
RESULTS: The average LOS for patients without a discrepancy between the admitting and discharge diagnosis codes, adjusted for comorbid conditions, was 3.4 days compared to 4.2 days with a discrepancy (0.76-day increase; P < 0.01).
CONCLUSIONS: Diagnosis discrepancy is associated with longer LOS. Diagnosis discrepancy on admission may be a marker of diagnosis uncertainty or poor patient assessment/documentation. Further research is needed to understand the underlying reasons for this discrepancy and its association with LOS, and, potentially, clinical outcomes. (c) 2009 Society of Hospital Medicine.

Entities:  

Mesh:

Year:  2009        PMID: 19388065     DOI: 10.1002/jhm.453

Source DB:  PubMed          Journal:  J Hosp Med        ISSN: 1553-5592            Impact factor:   2.960


  9 in total

1.  The derivation and validation of a simple model for predicting in-hospital mortality of acutely admitted patients to internal medicine wards.

Authors:  Ali Sakhnini; Walid Saliba; Naama Schwartz; Naiel Bisharat
Journal:  Medicine (Baltimore)       Date:  2017-06       Impact factor: 1.889

2.  Point-of-care ultrasonography in Norwegian out-of-hours primary health care.

Authors:  Kjetil Myhr; Hogne Sandvik; Tone Morken; Steinar Hunskaar
Journal:  Scand J Prim Health Care       Date:  2017-06-08       Impact factor: 2.581

3.  Study protocol for a prospective, double-blinded, observational study investigating the diagnostic accuracy of an app-based diagnostic health care application in an emergency room setting: the eRadaR trial.

Authors:  S Fatima Faqar-Uz-Zaman; Natalie Filmann; Dora Mahkovic; Michael von Wagner; Charlotte Detemble; Ulf Kippke; Ursula Marschall; Luxia Anantharajah; Philipp Baumartz; Paula Sobotta; Wolf O Bechstein; Andreas A Schnitzbauer
Journal:  BMJ Open       Date:  2021-01-08       Impact factor: 2.692

4.  Diagnostic Uncertainty in Dyspneic Patients with Cancer in the Emergency Department.

Authors:  Katherine M Hunold; Jeffrey M Caterino; Jason J Bischof
Journal:  West J Emerg Med       Date:  2021-01-29

5.  Development of a scoring tool for predicting prolonged length of hospital stay in peritoneal dialysis patients through data mining.

Authors:  Jingyi Wu; Guilan Kong; Yu Lin; Hong Chu; Chao Yang; Ying Shi; Haibo Wang; Luxia Zhang
Journal:  Ann Transl Med       Date:  2020-11

6.  Describing agreement in the main condition coding field using Canadian ICD-11 inpatient data.

Authors:  Natalie Wiebe; Hude Quan; Danielle A Southern; Chelsea Doktorchik; Catherine Eastwood
Journal:  Int J Popul Data Sci       Date:  2021-10-19

7.  Automated identification of diagnostic labelling errors in medicine.

Authors:  Wolf E Hautz; Moritz M Kündig; Roger Tschanz; Tanja Birrenbach; Alexander Schuster; Thomas Bürkle; Stefanie C Hautz; Thomas C Sauter; Gert Krummrey
Journal:  Diagnosis (Berl)       Date:  2021-10-21

8.  The discrepancy between admission and discharge diagnoses: Underlying factors and potential clinical outcomes in a low socioeconomic country.

Authors:  Samar Fatima; Sara Shamim; Amna Subhan Butt; Safia Awan; Simra Riffat; Muhammad Tariq
Journal:  PLoS One       Date:  2021-06-15       Impact factor: 3.240

9.  Discrepancy between Admission and Discharge Diagnoses in Central Serbia: Analysis by the Groups of International Classification of Diseases, 10th Revision.

Authors:  Natasa Mihailovic; Dragan Vasiljevic; Vesna Milicic; Marina Luketina Sunjka; Snezana Radovanovi; Biljana Milicic; Sanja Kocic
Journal:  Iran J Public Health       Date:  2020-12       Impact factor: 1.429

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.