Pharmaceutical cost management in an ambulatory setting using a risk adjustment tool

BMC Health Services Research

Laia Buigues-Pastor¹  Carla Sancho-Mestre²  José-Luis Trillo-Mata¹  Natividad Guadalajara-Olmeda²  Ruth Usó-Talamantes¹  David Vivas-Consuelo² 

[1]Valencian Health Department (Conselleria de Sanitat), General Directorate of Pharmacy and Pharmaceutical Products, Valencia, Spain

[2]Research Centre for Health Economics and Management, Universitat Politècnica de València, Edificio 7 J, Campus de Vera s/n, 46022 Valencia, Spain

关键词: Capitation payments;    Clinical Risk Groups;    Chronic condition;    Pharmaceutical expenditure;    Predictive models;    Risk adjustment;   

Others  :  1125870
DOI  :  10.1186/1472-6963-14-462

 received in 2013-11-27, accepted in 2014-09-16,  发布年份 2014

【 摘 要 】

Background

Pharmaceutical expenditure is undergoing very high growth, and accounts for 30% of overall healthcare expenditure in Spain. In this paper we present a prediction model for primary health care pharmaceutical expenditure based on Clinical Risk Groups (CRG), a system that classifies individuals into mutually exclusive categories and assigns each person to a severity level if s/he has a chronic health condition. This model may be used to draw up budgets and control health spending.

Methods

Descriptive study, cross-sectional. The study used a database of 4,700,000 population, with the following information: age, gender, assigned CRG group, chronic conditions and pharmaceutical expenditure. The predictive model for pharmaceutical expenditure was developed using CRG with 9 core groups and estimated by means of ordinary least squares (OLS). The weights obtained in the regression model were used to establish a case mix system to assign a prospective budget to health districts.

Results

The risk adjustment tool proved to have an acceptable level of prediction (R² ≥ 0.55) to explain pharmaceutical expenditure. Significant differences were observed between the predictive budget using the model developed and real spending in some health districts. For evaluation of pharmaceutical spending of pediatricians, other models have to be established.

Conclusion

The model is a valid tool to implement rational measures of cost containment in pharmaceutical expenditure, though it requires specific weights to adjust and forecast budgets.

【 授权许可】

   
2014 Vivas-Consuelo et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

【 参考文献 】

• [1]Hux JE, Naylor CD: Drug prices and third party payment: do they influence medication selection? Pharmacoecon 1994, 5(4):343-350.
• [2]Sicras-Mainar A, Serrat-Tarres J, Navarro-Artieda R, Llopart-Lopez J: [Prospects of adjusted clinical groups (ACG’s) in capitated payment risk adjustment]. Rev Esp Salud Publica 2006, 80(1):55-65.
• [3]Mossey JM, Roos LL: Using insurance claims to measure health-status - the illness scale. J Chronic Dis 1987, 40:S41-S50.
• [4]Newhouse JP, Manning WG, Keeler EB, Sloss EM: Adjusting capitation rates using objective health measures and prior utilization. Health Care Financ Rev 1989, 10(3):41-54.
• [5]Ash A, Porell F, Gruenberg L, Sawitz E, Beiser A: Adjusting Medicare capitation payments using prior hospitalization data. Health Care Financ Rev 1989, 10(4):17-29.
• [6]Ellis RP, Pope GC, Iezzoni L, Ayanian JZ, Bates DW, Burstin H, Ash AS: Diagnosis-based risk adjustment for Medicare capitation payments. Health Care Financ Rev 1996, 17(3):101-128.
• [7]Pope GC, Kautter J, Ellis RP, Ash AS, Ayanian JZ, Lezzoni LI, Ingber MJ, Levy JM, Robst J: Risk adjustment of Medicare capitation payments using the CMS-HCC model. Health Care Financ Rev 2004, 25(4):119-141.
• [8]Starfield B, Weiner J, Mumford L, Steinwachs D: Ambulatory care groups: a categorization of diagnoses for research and management. Health Serv Res 1991, 26(1):53-74.
• [9]Weiner JP, Starfield BH, Steinwachs DM, Mumford LM: Development and application of a population-oriented measure of ambulatory care case-mix. Med Care 1991, 29(5):452-472.
• [10]Hughes JS, Averill RF, Eisenhandler J, Goldfield NI, Muldoon J, Neff JM, Gay JC: Clinical Risk Groups (CRGs): a classification system for risk-adjusted capitation-based payment and health care management. Med Care 2004, 42(1):81-90.
• [11]Berlinguet M, Preyra C, Dean S: Comparing the Value of Three Main Diagnostic-Based Risk-Adjustment Systems (DBRAS). Ottawa, Ontario: Edited by Foundation CHSR; 2005.
• [12]Von Korff M, Wagner EH, Saunders K: A chronic disease score from automated pharmacy data. J Clin Epidemiol 1992, 45(2):197-203.
• [13]Malone DC, Billups SJ, Valuck RJ, Carter BL: Development of a chronic disease indicator score using a Veterans Affairs Medical Center medication database. IMPROVE Investigators. J Clin Epidemiol 1999, 52(6):551-557.
• [14]Clark DO, Von Korff M, Saunders K, Baluch WM, Simon GE: A chronic disease score with empirically derived weights. Med Care 1995, 33(8):783-795.
• [15]Lamers LM: Pharmacy costs groups: a risk-adjuster for capitation payments based on the use of prescribed drugs. Med Care 1999, 37(8):824-830.
• [16]Lamers LM: Health-based risk adjustment: is inpatient and outpatient diagnostic information sufficient? Inquiry 2001, 38(4):423-431.
• [17]Lamers LM, van Vliet RC: The Pharmacy-based Cost Group model: validating and adjusting the classification of medications for chronic conditions to the Dutch situation. Health Policy 2004, 68(1):113-121.
• [18]Lamers LM, Vliet RC: Health-based risk adjustment Improving the pharmacy-based cost group model to reduce gaming possibilities. Eur J Health Econ 2003, 4(2):107-114.
• [19]Johnson RE, Hornbrook MC, Nichols GA: Replicating the chronic disease score (CDS) from automated pharmacy data. J Clin Epidemiol 1994, 47(10):1191-1199.
• [20]Zhao Y, Ellis RP, Ash AS, Calabrese D, Ayanian JZ, Slaughter JP, Weyuker L, Bowen B: Measuring population health risks using inpatient diagnoses and outpatient pharmacy data. Health Serv Res 2001, 36(6 Pt 2):180-193.
• [21]Stam PJ, van Vliet RC, van de Ven WP: Diagnostic, pharmacy-based, and self-reported health measures in risk equalization models. Med Care 2010, 48(5):448-457.
• [22]Hanley GE, Morgan S, Reid RJ: Explaining prescription drug use and expenditures using the adjusted clinical groups case-mix system in the population of British Columbia, Canada. Can Med Care 2010, 48(5):402-408.
• [23]Aguado A, Guino E, Mukherjee B, Sicras A, Serrat J, Acedo M, Ferro JJ, Moreno V: Variability in prescription drug expenditures explained by adjusted clinical groups (ACG) case-mix: a cross-sectional study of patient electronic records in primary care. BMC Health Serv Res 2008, 8(4):11.
• [24]Garcia-Goni M, Ibern P: Predictability of drug expenditures: An application using morbidity data. Health Econ 2008, 17(1):119-126.
• [25]Garcia-Goni M, Ibern P, Inoriza JM: Hybrid risk adjustment for pharmaceutical benefits. Eur J Health Econ 2009, 10(3):299-308.
• [26]Vivas-Consuelo D, Uso-Talamantes R, Trillo-Mata JL, Caballer-Tarazona M, Barrachina-Martinez I, Buigues-Pastor L: Predictability of pharmaceutical spending in primary health services using Clinical Risk Groups. Health Policy 2014, 116(2–3):188-195.
• [27]Robst J, Levy JM, Ingber MJ: Diagnosis-based risk adjustment for medicare prescription drug plan payments. Health Care Financ Rev 2007, 28(4):15-30.
• [28]Zhao Y, Ash AS, Ellis RP, Ayanian JZ, Pope GC, Bowen B, Weyuker L: Predicting pharmacy costs and other medical costs using diagnoses and drug claims. Med Care 2005, 43(1):34-43.
• [29]Buchner F, Goepffarth D, Wasem J: The new risk adjustment formula in Germany: implementation and first experiences. Health Policy 2013, 109(3):253-262.
• [30]Inoriza JM, Coderch J, Carreras M, Vall-Llosera L, Garcia-Goni M, Lisbona JM, Ibern P: [Measurement of morbidity attended in an integrated health care organization]. Gac Sanit 2009, 23(1):29-37.
• [31]Orueta JF, Mateos Del Pino M, Barrio Beraza I, Nuno Solinis R, Cuadrado Zubizarreta M, Sola Sarabia C: [Stratification of the population in the Basque Country: results in the first year of implementation.]. Aten Primaria 2012, 45(1):54-60.
• [32]Sicras-Mainar A, Navarro-Artieda R: [Validating the Adjusted Clinical Groups [ACG] case-mix system in a Spanish population setting: a multicenter study]. Gac Sanit 2009, 23(3):228-231.
• [33]Omar RZ, O’Sullivan C, Petersen I, Islam A, Majeed A: A model based on age, sex, and morbidity to explain variation in UK general practice prescribing: cohort study. BMJ 2008, 337:a238.
• [34]Caballer-Tarazona M, Buigues-Pastor L, Saurí- Ferrer I, Uso-Talamantes R, Trillo-Mata JL: [A standardized amount indicator by equivalent patient to control outpatient pharmaceutical expenditure, Spain]. Rev Esp Salud Publica 2011, 86:371-380.
• [35]De la Poza-Plaza E, Barrachina I, Trillo-Mata J, Uso-Talamantes R: Sistema de Prescripción y dispensación electrónica en la Agencia Valenciana de Salud. El Prof de la Inf 2011, 20:9.
• [36]Vivas D, Guadalajara N, Barrachina I, Trillo JL, Uso R, De-la-Poza E: Explaining primary healthcare pharmacy expenditure using classification of medications for chronic conditions. Health Policy 2011, 103(1):9-15.
• [37]Buntin MB, Zaslavsky AM: Too much ado about two-part models and transformation? Comparing methods of modeling Medicare expenditures. J Health Econ 2004, 23(3):525-542.
• [38]Duan N: Smearing estimate - a nonparametric retransformation method. J Am Stat Assoc 1983, 78(383):605-610.
• [39]Calderon-Larranaga A, Abrams C, Poblador-Plou B, Weiner JP, Prados-Torres A: Applying diagnosis and pharmacy-based risk models to predict pharmacy use in Aragon, Spain: the impact of a local calibration. BMC Health Serv Res 2010, 10:22. BioMed Central Full Text