Rational Classification of Simple Disease Cases in Bangkok Dusit Medical Services Hospitals using Relative Weight and Case Mixed Index

Main Article Content

Chatree Duangnet
Jinhatha Panyasorn
Noppadol Phengpinit
Warangkana Jarunai
Natamon Worachat
Ritthikrai Taweecharoen
Chaiyos Kunanusont

Abstract




OBJECTIVES: This study proposes using relative weights (RW) of specific health conditions or diagnoses as a simple tool to differentiate simple from not-simple disease cases.


MATERIALAND METHODS: Using data from 1,558 records of closed chart review, conducted by Utilization Management and Third-party-payer Services (UM) of Bangkok Dusit Medical Services (BDMS) and a list of simple diseases advised by five health insurance partners, we compared discriminating power of various RW levels. Scenario assessment was conducted to quantify number of cases that could be missed out and turn into risk management (RM) cases.


RESULT: RW of 0.3 could categorize 22 (71%) of 31 conditions as simple diseases while RW of 0.4 had the power to categorize 27 (87%) and RW of 0.8 could categorize 31 (100%) of the whole list. Scenario assessment showed increasing risk management (RM) cases from 8 cases using RW = 0.3 to 42 cases (5.3 folds) using RW = 0.4 and to 141 cases (17.6 folds) when RW = 0.8 was used. Although greater RW threshold could capture greater proportion of simple diseases, it could result exponentially increase of RM cases. As a result, RW = 0.4 would be the optimum and practical cut-off point to differentiate simple from not-simple diseases. To monitor hospital performance, different levels of “Percentage of simple disease” should be applied due to different complexity (indicated by case mixed index-CMI) of hospitals in BDMS network. We propose “Yellow zones” of 10%-20% for super tertiary care (CMI > 2.0), 40% - 50% for hub tertiary care (CMI 1.00 – 1.99), 60% - 70% for basic tertiary care (CMI 0.50 – 0.99) and 70% - 80% for secondary care (CMI < 0.5) hospitals. Only continuous monitoring is required when performance stays below these yellow zones. However, when the proportion of simple disease is in the yellow zones, hospital management should pay specific attention. If the situation progressed and theproportion is higher than the upper limit of the yellow zone, special interventions of hospital management and insurance partners would be urgently required.


CONCLUSION: Our data showed that RW of 0.4 was the optimum threshold to differentiate simple from not-simple diseases as it could cover 87% of the current list of simple diseases with minimum number of possible RM cases. Different ranges of proportions of simple diseases are proposed for different complexity of BDMS hospitals according to their case complexity, reflected by case mixed index (CMI). More studies using data from other networks are recommended for broader application of this concept.




Downloads

Download data is not yet available.

Article Details

How to Cite
1.
Duangnet C, Panyasorn J, Phengpinit N, Jarunai W, Worachat N, Taweecharoen R, Kunanusont C. Rational Classification of Simple Disease Cases in Bangkok Dusit Medical Services Hospitals using Relative Weight and Case Mixed Index. BKK Med J [Internet]. 2019Sep.20 [cited 2020Jul.15];15(2):130. Available from: https://he02.tci-thaijo.org/index.php/bkkmedj/article/view/222604
Section
Original Article

References

1. Gertman PM, Restuccia JD. The appropriateness evaluationprotocol: a technique for assessing unnecessary days ofhospital care. Med Care. 1981;19(8):855-71.
2. Poulos CJ, Magee C, Bashford G, et al. Determining level ofcare appropriateness in the patient journey from acute care torehabilitation. BMC Health Serv Res. 2011;11:291.
3. Gertman PM, Restuccia JD. The appropriateness evaluationprotocol: a technique for assessing unnecessary days ofhospital care. Med Care. 1981;19(8):855-71.
4. Tavakoli N, Hosseini Kasnavieh SM, Yasinzadeh M, et al.Evaluation of Appropriate and Inappropriate Admission andHospitalization Days According to Appropriateness EvaluationProtocol (AEP). Arch Iran Med. 2015;18(7):430-4.
5. Payne SM. Identifying and managing inappropriate hospitalutilization: a policy synthesis. Health Serv Res. 1987;22(5):709-69.
6. Kossovsky MP, Chopard P, Bolla F, et al. Evaluation ofquality improvement interventions to reduce inappropriatehospital use. Int J Qual Health Care. 2002;14(3):227-32.
7. PrakashL. Grover. Supplement: Inappropriate Use of AcuteHospital Care: Extent, Causes, and Ameliorative Approaches|| Is Inappropriate Hospital Care an Inevitable Component ofthe Health Care System? Medical Care. 1991;29(8):As1.
8. Siu AL, Manning WG, Benjamin B. Patient, provider andhospital characteristics associated with inappropriatehospitalization. Am J Public Health. 1990;80(10):1253-6.
9. Moya-Ruiz C, Peiró S, Meneu R. Effectiveness of feedbackto physicians in reducing inappropriate use of hospitalization:a study in a Spanish hospital. Int J Qual Health Care.2002;14(4):305-12.
10. Rivers PA, Tsai KL. Managing costs and managing care. IntJ Health Care Qual Assur Inc Leadersh Health Serv.2001;14(6-7):302-7.
11. Wickizer TM, Lessler D. Utilization management: issues,effects, and future prospects. Annu Rev Public Health.2002;23:233-54.
12. Restuccia JD. The evolution of hospital utilization reviewmethods in the United States. Int J Qual Health Care.1995;7(3):253-60.
13. Restuccia JD, Gertman P. A comparative analysis ofappropriateness of hospital use. Health Aff (Millwood).1984;3(2):130-8.
14. Hammond CL, Pinnington LL, Phillips MF. A qualitativeexamination of inappropriate hospital admissions and lengthsof stay. BMC Health Serv Res. 2009;9:44.
15. Soria-Aledo V, Carrillo-Alcaraz A, Flores-Pastor B, et al.Reduction in inappropriate hospital use based on analysis ofthe causes. BMC Health Serv Res. 2012;12:361.
16. Zhang Y, Chen Y, Zhang X, et al. Current level and determinantsof inappropriate admissions to township hospitals under thenew rural cooperative medical system in China: a crosssectionalstudy. BMC Health Serv Res. 2014;14:649.
17. Arrow KJ. Uncertainty and the welfare economics of medicalcare. 1963. Bull World Health Organ. 2004;82(2):141-9.
18. van Dijk CE, van den Berg B, Verheij RA, et al. Moral hazardand supplier-induced demand: empirical evidence in generalpractice. Health Econ. 2013;22(3):340-52.
19. Dong Y. How Health Insurance Affects Health Care Demand:A Structural Analysis of Behavioral Moral Hazard and AdverseSelection. Economic Inquiry 2013;51(2):1324-44.
20. Folland S, Goodman AC, Stano M. The Economics of Healthand Health Care., 2012.
21. Zhang Y, Zhang L, Li H, et al. Determinants of InappropriateAdmissions in County Hospitals in Rural China: ACross-Sectional Study. Int J Environ Res Public Health.2018;15(6)
22. Brabrand M, Knudsen T, Hallas J. The characteristics andprognosis of patients fulfilling the Appropriateness EvaluationProtocol in a medical admission unit; a prospective observationalstudy. BMC Health Serv Res 2011;11:152.
23. Baré ML, Prat A, Lledo L, et al. Appropriateness of admissionsand hospitalization days in an acute-care teaching hospital.Rev Epidemiol Sante Publique. 1995;43(4):328-36.
24. Fetter RB, Shin Y, Freeman JL, et al. Case mix definition bydiagnosis-related groups. Med Care. 1980;18(2 Suppl):iii, 1-53.
25. Prakornsri N, Amornyingcharoen W. Comparison of healthcarecharges and reimbursement amount to hospital based ondiagnosis related group (DRG) in Thai patients admitted togovernment hospitals. Mahidol University; 2006.
26. Mendez CM, Harrington DW, Christenson P, et al. Impact ofhospital variables on case mix index as a marker of diseaseseverity. Popul Health Manag. 2014;17(1):28-34.
27. Claudio P. Severity of illness in the case-mix specification andperformance: A study for Italian public hospitals PintoClaudio. J Hospital Administ. 2014;3(1):33.
28. Langenbrunner bJC, Cashin C, O’Dougherty S. Designingand Implementing Health Care Provider Payment Systems:How-To Manuals. 1 ed: World Bank Publications; 2009.
29. Guterman S, Davis K, Schoenbaum S, et al. Using Medicarepayment policy to transform the health system: a frameworkfor improving performance. Health Aff (Millwood).2009;28(2):w238-50.
30. Barati M, Azami F, Nagdi B, et al. Moral Hazards in ProvidingHealth Services: A Review of Studies. Evidence Based HealthPolicy, Management & Economics Health Policy ResearchCenter, Shahid Sadoughi University of Medical Sciences.2018;2(1):69.
31. Yip WC, Hsiao W, Meng Q, et al. Realignment of incentivesf o r h e a l t h - c a r e p r o v i d e r s i n C h i n a . Lanc e t .2010;375(9720):1120-30.
32. Horn SD. Measuring severity of illness: comparisons acrossinstitutions. Am J Public Health. 1983;73(1):25-31.
33. Horn SD, Horn RA, Sharkey PD. The Severity of Illness Indexas a severity adjustment to diagnosis-related groups. HealthCare Financ Rev 1984;Suppl:33-45.
34. Horn SD, Sharkey PD. Measuring Severity of Illness toPredict Patient Resource Use Within DRGs. Inquiry.1983;20:321