Analyzing Hospital High Length of Stay Outliers in Turkey

Yıl 2022, Cilt: 6 Sayı: 3, 903 - 912, 29.09.2022

Pakize Yıgıt , Sabahattin Aydın , Hasan Güler

https://doi.org/10.30621/jbachs.1159299

Öz

Purpose: The aim of this study is to examine length of stay (LOS) outliers by analyzing hospital administrative database.

Material and Methods: The Turkish Ministry of Health DRG grouper database was utilized to obtain hospital administrative data on discharges for 15 training and research hospitals in 2012. For each diagnosis-related group (DRG), the geometric mean plus two standard deviations were calculated to identify outliers. Analyses were conducted using descriptive statistics and logistic regression using generalized estimating equations (GEE).


Results: High LOS outliers found to be 4.4 % of the cases, they were responsible for 24.50 percent of all discharge days. Alcohol, drug use disorders, burns, and diseases of the ear, nose, mouth, and throat were the factors that had the greatest impact on high LOS outliers, according to the multivariate model.


Conclusion: A quarter of all inpatient days are made up of LOS outliers. Burns, neonate cases, and alcohol/drug use issues should all be carefully evaluated. In order to improve clinical quality and effectively manage hospital resources, hospital administrators and health policy makers should take length of stay outliers into consideration.

Anahtar Kelimeler

Case mix, Diagnosis-Related Groups, DRG Outliers, Hospital Financial Management, Length of Stay

Kaynakça

• 1. Fetter RB. Diagnosis Related Groups: Understanding Hospital Performance. Interfaces (Providence). 1991;21(1):6–26.
• 2. Fetter RB, Shin Y, Freeman JL, Averill RF, Thompson JD. Case mix definition by diagnosis-related groups. Med Care. 1980;18(2):1–53.
• 3. Hughes AH, Horrocks D, Leung C, Richardson MB, Sheehy AM, Locke CFS. The increasing impact of length of stay “outliers” on length of stay at an urban academic hospital. BMC Health Serv Res. 2021;21(1):1–7.
• 4. Antioch KM, Ellis RP, Gillett S, Borovnicar D, Marshall RP. Risk adjustment policy options for casemix funding: International lessons in financing reform. Eur J Heal Econ. 2007;8(3):195–212.
• 5. Russell-Weisz D, Hindle D. High length-of-stay outliers under casemix funding of a remote rural community with a high proportion of aboriginal patients. Aust Health Rev. 2000;23(2):47–61.
• 6. Palmer G, Reid B. Evaluation of the performance of diagnosis-related groups and similar ... Heal Serv Manag Res. 2001;14(2):71–81.
• 7. Pirson M, Dramaix M, Leclercq P, Jackson T. Analysis of cost outliers within APR-DRGs in a Belgian general hospital: Two complementary approaches. Health Policy (New York). 2006;76(1):13–25.
• 8. Freitas A, Silva-Costa T, Lopes F, Garcia-Lema I, Teixeira-Pinto A, Brazdil P, et al. Factors influencing hospital high length of stay outliers. BMC Health Serv Res. 2012;12(1).
• 9. Camilleri C, Jofre-Bonet M, Serra-Sastre V. The suitability of a DRG casemix system in the Maltese hospital setting. Health Policy (New York) [Internet]. 2018;122(11):1183–9. Available from: https://doi.org/10.1016/j.healthpol.2018.08.002
• 10. Cots F, Chiarello P, Salvador X, Castells X, Quentin W. Diagnosis-Related Groups in Europe Moving towards transparency, efficiency and quality in hospitals. first. Busse R, Geissler A, Quentin W, Wiley M, editors. BMJ (Online). Berkshire: Mc Graw Hill Open University Press; 2011.
• 11. Cots F, Mercadé L, Castells X, Salvador X. Relationship between hospital structural level and length of stay outliers: Implications for hospital payment systems. Health Policy (New York). 2004;68(2):159–68.
• 12. Cots F, Elvira D, Castells X, Sáez M. Relevance of outlier cases in case mix systems and evaluation of trimming methods. Health Care Manag Sci. 2003;6(1):27–35.
• 13. Lee AH, Xiao J, Vemuri SR, Zhao Y. A discordancy test approach to identify outliers of length of hospital stay. Stat Med. 1998;17(19):2199–206.
• 14. Mehra T, Muller CTB, Volbracht J, Seifert B, Moos R. Predictors of high profit and high deficit outliers under SwissDRG of a tertiary care center. PLoS One. 2015;10(10):1–18.
• 15. Akdağ R. Tig Uygulama Rehberi [Internet]. Ankara: Sağlık Bakanlığı,; 2011. Available from: https://shgmsgudb.saglik.gov.tr/Eklenti/3621/0/teshis-iliskili-gruplar-uygulama-rehberi.pdf
• 16. Mendez CM, Harrington DW, Christenson P, Spellberg B. Impact of hospital variables on case mix index as a marker of disease severity. Popul Health Manag. 2014;17(1):28–34.
• 17. Lin S, Rouse P, Wang YM, Zhang F. A Statistical Model to Detect DRG Outliers. IEEE Access. 2022;10:28717–24.
• 18. Medarevic AP. Describing serbian hospital activity using Australian refined diagnosis related groups: A case study in Vojvodina Province. Zdr Varst. 2020;59(1):18–26.
• 19. Lichtig L. Hospital information systems for case mix management. Health Policy (New York) [Internet]. 1986 [cited 2022 Jul 18];8(1):142–3. Available from: https://ideas.repec.org/a/eee/hepoli/v8y1987i1p142-143.html
• 20. Reid B, Palmer G, Aisbett C. The performance of Australian DRGs. Aust Health Rev. 2000;23(2):20–31.
• 21. Özkan O, Ağırbaş İ. Distribution of Ministry of Health Global Budget according to Diagnosis Related Groups. J Ankara Univ Fac Med. 2019;71(3):163–71.
• 22. Felder S. The variance of length of stay and the optimal DRG outlier payments. Int J Health Care Finance Econ. 2009;9(3):279–89.
• 23. Ghaffari S, Jackson TJ, Doran CM, Wilson A, Aisbett C. Describing Iranian hospital activity using Australian Refined DRGs: A case study of the Iranian Social Security Organisation. Health Policy (New York). 2008;87(1):63–71.
• 24. Vogl M. Assessing DRG cost accounting with respect to resource allocation and tariff calculation: The case of Germany. Health Econ Rev. 2012;2(1):1–12.
• 25. Kuwabara K, Imanaka Y, Matsuda S, Fushimi K, Hashimoto H, Ishikawa KB, et al. The association of the number of comorbidities and complications with length of stay, hospital mortality and LOS high outlier, based on administrative data. Environ Health Prev Med. 2008;13(3):130–7.
• 26. Cyganska M. The Impact Factors on the Hospital High Length of Stay Outliers. Procedia Econ Financ [Internet]. 2016;39(November 2015):251–5. Available from: http://dx.doi.org/10.1016/S2212-5671(16)30320-3
• 27. Lindberg DS, Prosperi M, Bjarnadottir RI, Thomas J, Crane M, Chen Z, et al. Identification of important factors in an inpatient fall risk prediction model to improve the quality of care using EHR and electronic administrative data: A machine-learning approach. Int J Med Inform [Internet]. 2020;143:104272. Available from: https://doi.org/10.1016/j.ijmedinf.2020.104272
• 28. Hashimoto R, Brodt E, Skelly A, Dettori J. Administrative Database Studies: Goldmine or Goose Chase? Evid Based Spine Care J. 2014;05(02):074–6.