Agreement between admission and discharge diagnoses: analysis by the groups of International Classification of Diseases, 10th revision

Nataša M Mihailović; Goran Trajković; Ivana Simić-Vukomanović; Svetlana Ristić; Sanja Kocić

doi:10.2298/VSP150427057M

Nataša M Mihailović Institute of Public Health Kragujevac, Serbia Center for Biostatistics and Informatics
Goran Trajković Institute for Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, Serbia
Ivana Simić-Vukomanović Institute of Public Health Kragujevac, Kragujevac, Serbia
Svetlana Ristić Institute for Oncology and Radiology of Serbia, Belgrade, Serbia
Sanja Kocić Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia

DOI: https://doi.org/10.2298/VSP150427057M

Keywords: patient admission, patient discharge, diagnosis, international classification of diseases,

Abstract

Background/Aim. Admission diagnosis represents the diagnosis of an illness, injury or condition due to which a patient is referred to hospital to be admitted. Discharge diagnosis represents the main reason of illness or condition due to which a patient is admitted. The aim of this study was to analyze the agreement between admission diagnostic groups and discharge diagnostic groups of patients in the Clinical Center Kragujevac in the period from January 1, 2006 to December 31, 2013 on the basis of the hospitalization report. Methods. From the basic set of reports, 5% of random samples were singled out and they contained 20,422 reports. Out of the given number of reports, 18,173 hospitalization reports were complete and then further analyzed in the paper. Admission diagnostic groups given by the primary care doctor were compared with discharge diagnostic groups filled out by the practicing physician in the hospital ward from which a patient was discharged. The agreement of these two diagnostic groups was an indication of the high-quality performance of the primary care doctor. Agreement analysis was conducted using Cohen’s Kappa statistics. Results. Agreement analysis showed that the values of the Kappa coefficient for the five leading admission diagnostic groups were in the range of κ = 0.61 to κ = 0.94. The values of the Kappa coefficient for the five most common discharge diagnostic groups were in the range of κ = 0.55 to κ = 0.81. Conclusion. Hospitalization report is a reliable individual report on inpatient care, so it could be used in determining the degree of agreement between admission diagnostic groups and discharge diagnostic groups.

Author Biography

Nataša M Mihailović, Institute of Public Health Kragujevac, Serbia Center for Biostatistics and Informatics

Doctor, Specialist of Medical Statistics and Informatics

References

Ellekjaer H, Holmen J, Kruger J, Terent A. Identification of Inci-dent Stroke in Norway. Hospital Discharge Data Compared With a Population-Based Stroke Register. Stroke 1999; 30(1): 56−60.

Schoenman JA, Sutton JP, Elixhauser A, Love D. Understanding and Enhancing the Value of Hospital Discharge Data. Med Care Res Rev 2007; 64(4): 449−68.

Gray BH, Clement JP. Databases for Research on Nonprofit Health Care Organizations: Opportunities and Limitations. Am Behav Sci 2002; 45(10): 1550−91.

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 Epi-demiol 1999; 52(2): 137−42.

National Association of State Health Data Organizations (NAHDO). Consumer-Purchaser Disclosure Project. The state experience in health quality data collection. Washington, DC: National Partnership for Women and Families; 2004.

Roos LL, Gupta S, Soodeen RA, Jebamani L. Data quality in an in-formation-rich environment: Canada as an example. Can J Aging 2005; 24(1): 153−70.

McGinn T, Wyer PC, Newman TB, Keitz S, Leipzig R, For GG. Tips for learners of evidence-based medicine: 3. Measures of observer variability (kappa statistic). CMAJ 2004; 171(11): 1369−73.

Juurlink D, Preyra C, Croxford R, Chong A, Austin P, Tu J, et al. . Canadian Institute for Health Information Discharge Abstract Database: A Validation Study. Toronto: Institute for Clinical Evaluative Sciences; 2006.

Veras CM, Martins MS. Reliability of data from Authorization Forms for Hospital Admittance, Rio de Janeiro, Brazil. Cad Saude Publica 1994;10(3): 339−55. (Portuguese)

Mathias TA, Soboll ML. Reliability of diagnoses on authoriza-tion forms for hospital admission. Rev Saude Publica 1998; 32(6): 526−32. (Portuguese)

Buckley C, Kearney P, Ali F, Bhuachalla C, Casey C, Roberts G, et al. Concordance studies between hospital discharge data and medical records for the recording of lower extremity amputa-tion and diabetes in the Republic of Ireland. BMC Res Notes 2013; 6: 148.

Johnson T, Mcnutt R, Odwazny R, Patel D, Baker S. Discrepancy between admission and discharge diagnoses as a predictor of hospital length of stay. J Hosp Med 2009; 4(4): 234−9.

Farchi S, Camilloni L, Rossi PG, Chini F, Lori G, Tancioni V, et al. Agreement Between Emergency Room and Discharge Diag-noses in a Population of Injured Inpatients: Determinants and Mortality. J Trauma 2007; 62(5): 1207−14.

Lim GH, Seow E, Koh G, Tan D, Wong HP. Study on the dis-crepancies between the admitting diagnoses from the emer-gency department and the discharge diagnoses. Hong Kong J Emerg Med 2002; 9(2): 78−82.

Graff LG, Wang Y, Borkowski B, Tuozzo K, Foody J, Krumholz H, et al. Delay in the diagnosis of acute myocardial infarction: Ef-fect on quality of care and its assessment. Acad Emerg Med 2006; 13(9): 931−8.

McNutt R, Johnson T, Kane J, Ackerman M, Odwazny R, Bardhan J. Cost and quality implications of discrepancies between admit-ting and discharge diagnoses. Qual Manag Health Care 2012; 21(4): 220−7.