Literature DB >> 6418985

Data quality. An illustration of its potential impact upon a diagnosis-related group's case mix index and reimbursement.

H D Doremus, E M Michenzi.   

Abstract

The Health Care Finance Administration has developed a Medicare reimbursement methodology that will include an adjustment factor for hospital case mix. The patient classification scheme proposed for use in determining a hospital's case mix is the AUTOGRP Diagnosis-Related Groups (DRG) methodology developed at Yale University. The reliability of a case mix measure calculated using the DRG methodology is dependent on complete and accurate diagnostic and surgical data. The source of this data for the HCFA data base (MEDPAR) is the Medicare billing form, which is based on the patient medical record. Data from the MEDPAR file, the original medical record discharge order, and a reabstracted record are compared and analyzed for their effect upon DRG classification and the resultant Medicare reimbursement ceiling for one large teaching hospital. The study results show widely divergent diagnostic and surgical data that results in a significant variation in DRG classification and reimbursement ceilings.

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Mesh:

Year:  1983        PMID: 6418985

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  12 in total

1.  Agreement between self-reported and routinely collected health-care utilization data among seniors.

Authors:  Parminder Raina; Vicki Torrance-Rynard; Micheline Wong; Christel Woodward
Journal:  Health Serv Res       Date:  2002-06       Impact factor: 3.402

2.  Catch Me if You Can: Acute Events Hidden in Structured Chronic Disease Diagnosis Descriptions Show Detectable Recording Patterns in EHR.

Authors:  Franck Diaz-Garelli; Kristin M Lenoir; Brian J Wells
Journal:  AMIA Annu Symp Proc       Date:  2021-01-25

3.  Measuring diagnoses: ICD code accuracy.

Authors:  Kimberly J O'Malley; Karon F Cook; Matt D Price; Kimberly Raiford Wildes; John F Hurdle; Carol M Ashton
Journal:  Health Serv Res       Date:  2005-10       Impact factor: 3.402

4.  Intelligent Medical Record--entry (IMR-E).

Authors:  D Trace; F Naeymi-Rad; D Haines; J J Robert; F deSouza Almeida; L Carmony; M Evans
Journal:  J Med Syst       Date:  1993-08       Impact factor: 4.460

5.  Quality of the information contained in the minimum basic data set: results from an evaluation in eight hospitals.

Authors:  J E Calle; P J Saturno; P Parra; J Rodenas; M J Pérez; F S Eustaquio; E Aguinaga
Journal:  Eur J Epidemiol       Date:  2000       Impact factor: 8.082

6.  The trends in treatment of femoral neck fractures in the Medicare population from 1991 to 2008.

Authors:  Benjamin J Miller; Xin Lu; Peter Cram
Journal:  J Bone Joint Surg Am       Date:  2013-09-18       Impact factor: 5.284

7.  DRGs and severity of illness measures: an analysis of patient classification systems.

Authors:  M D Rosko
Journal:  J Med Syst       Date:  1988-08       Impact factor: 4.460

8.  Workflow Differences Affect Data Accuracy in Oncologic EHRs: A First Step Toward Detangling the Diagnosis Data Babel.

Authors:  Franck Diaz-Garelli; Roy Strowd; Virginia L Lawson; Maria E Mayorga; Brian J Wells; Thomas W Lycan; Umit Topaloglu
Journal:  JCO Clin Cancer Inform       Date:  2020-06

9.  What Oncologists Want: Identifying Challenges and Preferences on Diagnosis Data Entry to Reduce EHR-Induced Burden and Improve Clinical Data Quality.

Authors:  Franck Diaz-Garelli; Roy Strowd; Tamjeed Ahmed; Thomas W Lycan; Sean Daley; Brian J Wells; Umit Topaloglu
Journal:  JCO Clin Cancer Inform       Date:  2021-05

10.  A research paradigm for severity for illness: issues for the diagnosis-related group system.

Authors:  P M Gertman; S Lowenstein
Journal:  Health Care Financ Rev       Date:  1984
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