Discrepancy between Admission and Discharge Diagnoses in Central Serbia: Analysis by the Groups of International Classification of Diseases, 10th Revision
-
Natasa MIHAILOVIC
,
Dragan VASILJEVIC
,
Vesna MILICIC
,
Marina LUKETINA SUNJKA
,
Snezana RADOVANOVI
,
Biljana MILICIC
,
Sanja KOCIC
Abstract
Background: Repeated research while using the same methodology can be useful and it can enable relevant conclusions in the same health care system. The aim of our study was to perform comparative analysis of the agreement between admission and discharge diagnostic groups in period 2014-2017 with period 2006-2013 in the Clinical Center of Kragujevac, Serbia.
Methods: The 5% simple, random sample was made from the basic set of all hospital reports from Clinical Centre Kragujevac, Serbia, in the period 01.01. 2014 - 31.12. 2017 (n=10228). The first four digits of ICD-10 codes at admission and discharge were compared for agreement. We used discharge diagnosis as a "golden standard". Statistical analysis was performed using Cohen’s Kappa statistic.
Results: In the period 2014-2017, agreement between diagnosis among the most ICD10 groups increased in comparison with the period 2006-2013. Disagreements between diagnosis in the period 2014-2017 in comparation with period 2006-2013 was associated with increased length of stay in the hospital (7.5 vs. 9.1 days, P<0.01), patients were younger (54 vs 49.6 yr, P<0.01), number of males declined (26.3% vs 16.2%, P<0.05), kappa value decreased in XV ICD10 group and XI ICD10 group and kappa value increased in XIV ICD10 group.
Conclusion: Agreement between admission and discharge diagnosis among the most ICD10 diagnostic groups increased. Introduction of a new web application has increased the quality of data, but interpreting it requires the skill of researchers. Further research should identify modifiable causes of discrepancy between admission and discharge diagnoses.
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22. A Shrestha, D Zikos, L Fegaras. (2018). Sea-sonality of Discrepancies between Ad-mission and Discharge Diagnosis for Medicare Patients. Technologies, 6: 111.
23. Chen D, Liu S, Tan X, Zhao Q (2017). As-sessment of hospital length of stay and direct costs of type 2 diabetes in Hubei Province, China. BMC Health Serv Res, 17 (1): 199.
24. Agboado G, Peters J, Donkin L (2012). Fac-tors influencing the length of hospital stay among patients resident in Blackpool admitted with COPD: a cross-sectional study. BMJ Open, 2 (5): e000869.
25. Hughes LD, Witham MD (2018). Causes and correlates of 30 day and 180 day re-admission following discharge from a Medicine for the Elderly Rehabilitation unit. BMC Geriatr, 18 (1): 197.
26. McNutt R, Johnson T, Kane J, et al (2012). Cost and quality implications of discrep-ancies between admitting and discharge diagnoses. Qual Manag Health Care, 21(4):220-7.
27. Victor Sarli Issa, Layara Fernanda Lipari Di-nardi, Thiago Vicente Pereira, et al (2017). Diagnostic discrepancies in clinical prac-tice. An autopsy study in patients with heart failure. Medicine (Baltimore), 96(4): e5978.
28. Håheim LL, Helgeland J (2014). Agreement between referral information and dis-charge diagnoses according to Norwe-gian elective treatment guidelines – a cross-sectional study. BMC Health Services Research, 14: 493.
29. Fischer C, Anema HA, Klazinga NS (2012).The validity of indicators for as-sessing quality of care: a review of the European literature on hospital readmis-sion rate. Eur J Public Health, 22 (4): 484-91.
30. Nass SJ, Levit LA, Gostin LO (2009). Beyond the HIPAA Privacy Rule: Enhancing Privacy, Improving Health Through Research. 1en ed. Washington: The National Academies Press. Washington.
2. Ayanian JZ, Markel H (2016). Donabedian's Lasting Framework for Health Care Quality. N Engl J Med, 375 (3): 205-7
3. OECD (2016). The Organisation for Eco-nomic Co-operation and Development (OECD). Definitions for health care quality indicators 2016-2017 HCQI Data Collection. http://www.oecd.org/els/health-systems/Definitions-of-Health-Care-Quality-Indicators.pdf
4. Carinci F, Van Gool K, Mainz J, et al (2015). Towards actionable international compar-isons of health system performance: ex-pert revision of the OECD framework and quality indicators. Int J Qual Health Care, 27 (2): 137-46.
5. World Health Organization (2011). Interna-tional statistical classification of diseases and relat-ed health problems. 10th revision. 4th ed. Geneva: WHO Press. Switzerland.
6. Paoin W, Yuenyongsuwan M, Yokobori Y, et al (2018). Development of the ICD-10 simplified version and field test. Health Inf Manag, 47(2): 77-84.
7. Center for health informatics and biostatis-tics (2014). Institute of Public Health of Serbia "Dr Milan Jovanović Batut". In-structions for using a portal to form a database of stationary patients. www.batut.org.rs/download/uputstva/hospitalizacijaUputstvo03032014.pdf
8. Vlada Republike Srbije. Uredba o planu mreže zdravstvenih ustanova, Sl. glasnik RS, br. 2006-2018. https://www.paragraf.rs/propisi_download/uredba_o_planu_mreze_zdravstvenih_ustanova.pdf
9. Rattanaumpawan P, Wongkamhla T, Tham-likitkul V (2016). Accuracy of ICD-10 Coding System for Identifying Comor-bidities and Infectious Conditions Using Data from a Thai University Hospital Administrative Database. J Med Assoc Thai, 99(4): 368-73.
10. Mihailović N, Trajković G, Simić-Vukomanović I, et al (2016). Agreement between admission and discharge diag-noses: analysis by the groups of Interna-tional Classification of Diseases, 10th re-vision. Vojnosanit Pregl, 73 (12): 1125-31.
11. Johnson T, McNutt R, Odwazny R, et al (2009). Discrepancy between admission and discharge diagnoses as a predictor of hospital length of stay. J Hosp Med, 4 (4): 234-9.
12. AHRQ (2016). The Agency for Healthcare Research and Quality's (AHRQ). Data-bases Used for Hospital Quality Measures. Available from: www.ahrq.gov/professionals/quality-patient-safety/talkingquality/create/hospitals/databases.html
13. Boufford JI, Cassel CK, Bender KW, et al (2003). The Future of the Public's Health in the 21st Century. Washington: The National Academies Press. Columbia.
14. Oderkirk J, Ronchi E, Klazinga N (2013). International comparisons of health sys-tem performance among OECD coun-tries: Opportunities and data privacy pro-tection challenges. Health Policy, 112(1-2):9-18.
15. Mihailovic NM. The effect of health care uti-lization patterns in public and private sec-tor on patients' self-assessment of their health condition and satisfaction with the health care system in the Republic of Ser-bia [PhD thesis]. Faculty of Medical Sci-enes, University of Kragujevac, Kraguje-vac; 2017.
16. Maresso А, Mladovsky Р, Thomson S, et al (2015). Economic crisis, health systems and health in Europe country experience. 1nd ed. Denmark: European Observatory on Health Systems and Policies. Copenha-gen.
17. Catherine Foot C, Naylor Ch, Imison C (2010). The quality of GP diagnosis and referral. 1nd ed. UK: The King’s Fund. London.
18. Zwaan L, Singh H (2015). The challenges in defining and measuring diagnostic error. Diagnosis (Berl), 2(2): 97–103.
19. Ilgen JS, Humbert AJ, Kuhn G, et al (2012). Assessing diagnostic reasoning: a con-sensus statement summarizing theory, practice, and future needs. Acad Emerg Med, 19(12): 1454-61.
20. Hautz SC, Schuler L, Kämmer JE, et al (2016). Factors predicting a change in di-agnosis in patients hospitalised through the emergency room: a prospective ob-servational study. BMJ Open, 6(5): e011585.
21. Eames J, Eisenman A, Schuster RJ (2016). Disagreement between emergency de-partment admission diagnosis and hospi-tal discharge diagnosis: mortality and morbidity. Diagnosis (Berl), 3 (1): 23-30.
22. A Shrestha, D Zikos, L Fegaras. (2018). Sea-sonality of Discrepancies between Ad-mission and Discharge Diagnosis for Medicare Patients. Technologies, 6: 111.
23. Chen D, Liu S, Tan X, Zhao Q (2017). As-sessment of hospital length of stay and direct costs of type 2 diabetes in Hubei Province, China. BMC Health Serv Res, 17 (1): 199.
24. Agboado G, Peters J, Donkin L (2012). Fac-tors influencing the length of hospital stay among patients resident in Blackpool admitted with COPD: a cross-sectional study. BMJ Open, 2 (5): e000869.
25. Hughes LD, Witham MD (2018). Causes and correlates of 30 day and 180 day re-admission following discharge from a Medicine for the Elderly Rehabilitation unit. BMC Geriatr, 18 (1): 197.
26. McNutt R, Johnson T, Kane J, et al (2012). Cost and quality implications of discrep-ancies between admitting and discharge diagnoses. Qual Manag Health Care, 21(4):220-7.
27. Victor Sarli Issa, Layara Fernanda Lipari Di-nardi, Thiago Vicente Pereira, et al (2017). Diagnostic discrepancies in clinical prac-tice. An autopsy study in patients with heart failure. Medicine (Baltimore), 96(4): e5978.
28. Håheim LL, Helgeland J (2014). Agreement between referral information and dis-charge diagnoses according to Norwe-gian elective treatment guidelines – a cross-sectional study. BMC Health Services Research, 14: 493.
29. Fischer C, Anema HA, Klazinga NS (2012).The validity of indicators for as-sessing quality of care: a review of the European literature on hospital readmis-sion rate. Eur J Public Health, 22 (4): 484-91.
30. Nass SJ, Levit LA, Gostin LO (2009). Beyond the HIPAA Privacy Rule: Enhancing Privacy, Improving Health Through Research. 1en ed. Washington: The National Academies Press. Washington.
| Files | ||
| Issue | Vol 49 No 12 (2020) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/ijph.v49i12.4818 | |
| Keywords | ||
| Admission diagnosis Discharge diagnosis Hospital databases | ||
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How to Cite
1.
MIHAILOVIC N, VASILJEVIC D, MILICIC V, LUKETINA SUNJKA M, RADOVANOVI S, MILICIC B, KOCIC S. Discrepancy between Admission and Discharge Diagnoses in Central Serbia: Analysis by the Groups of International Classification of Diseases, 10th Revision. Iran J Public Health. 2020;49(12):2348-2355.
