A Review on Methods of Risk Adjustment and their Use in Integrated Healthcare Systems

Christin Juhnke; Susanne Bethge; Axel C Mühlbacher

doi:10.5334/ijic.2500

Figures & Tables

Table 1

Risk adjustment methods based on pharmaceutical information.

Method	Cell Approach vs. Regression Model	Risk Factors used	Development Objective	Developing Institution	Developed by (primary author)	Calculation	All-encounter Model	No. of Groups	Comorbidities included	Grouping	Clinical meaningful/interpretable
CDS	Aggregate model (Regression)	Age, Gender, Drug prescriptions	Measurement of chronic disease status	Center of Health Studies, Group Health Cooperative of Puget Sound, Seattle, WA, USA	Van Korff et al. [71] Clark et al. [70]	Prospective	No	28	Additive weights	Incomplete	Yes
Medicaid Rx	Aggregate model (Regression)	Age, Gender, Drug prescriptions for certain conditions	Development of risk-adjusted reimbursement/compensation systems for Medicaid	University of California, San Diego, CA, USA	Gilmer, Kronick, Dreifuß [22]	Prospective	No	45/48	Considered, cost weights are additive	Incomplete	Mostly yes
RxGroups	Aggregate model (Regression)	Age, Gender, Drug prescriptions	Risk-adjusted reimbursement/compensation systems for Medicare, Rrisk assessment, Efficiency audit of care providers, Calculation of premiums	Boston University, DxCG Inc., USA	Ash, Ellis, Pope et al.[65 72 73 74]	Prospective	No	155 (aggregated to 17 ARCs)	Hierarchical, additive weights for drugs of different hierarchies	Complete
RxRisk	Aggregate model (Regression)	Age, Gender, Drug prescriptions for certain conditions	Risk assessment, Evaluation of severity	Center of Health Studies, Group Health Cooperative of Puget Sound, Seattle, WA, USA	Fishman [75 76]; von Korff [70 71]	Prospective	No	60	Additive weights for drugs of different categories	Complete
PCG	Full hierarchy as cell approach	Regularly outpatient prescriptions of common drugs for chronic conditions	Risk structure compensation system for statutory health insurance in the Netherlands	Erasmus University, Rotterdam, The Netherlands	Lamers & van Kliet [77 78]	Prospective	No	13/23	Original: only the most cost-intense PCG considered; Since 2008: Individual weights are additive	Incomplete	Yes
PCG+DCG		Outpatient and inpatient prescriptions					Yes	12
DxCG Rx Groups	Aggregate model (Regression)	Age, Gender, Drug prescriptions, Diagnoses	Prediction of future health costs, Development of a comprehensive Rx classification	DxCG, Inc., MedStat Market Scan	Zhao et al.[66 79]	Prospective	Yes	127(current expansion: 118)	Diagnoses of diverse categories are taken into account	Complete	Mostly yes

Table 2

Risk adjustment methods based on diagnostic information.

Method	Cell Approach vs. Regression Model	Risk Factors used	Development Objective	Developing Institution	Developed by (primary author)	Calculation	All-encounter Model	No. of Groups	Comorbidities included	Grouping	Clinical meaningful/interpretable
ACG/ADG/ADG-PM	ACGs: cells approach, ADG-Hosdom: aggregate model (Regression)	Diagnoses, Age, Gender, Birthweight, Delivery	Evaluation of severity, Reimbursement/compensation of institutions	Johns Hopkins University, Baltimore, MD, USA	Starfield [83]; Weiner [44 45]; Anderson	Concurrent	ACG: yes ADG: no	ACG: 93 ACG-PM: 236	ACGs are based on combination of diseases	ACG/-PM: complete ADG: incomplete	ACG: no
HCC	Aggregate model (Regression)	Diagnoses, Age, Gender	Risk-adjusted reimbursement/compensation systems, Rrisk assessment	Boston University, USA	Ash, Ellis et al.	Prospective	Yes	184	Hierarchical, additive weights for non-related diseases	Complete	Yes
Rx HCC	Aggregate model (Regression)	Inpatient + outpatient diagnoses, Drug prescriptions	Calculation of individual risk scores	Medicare Part D, USA		Prospective	Yes	197	Hierarchical, additive weights	Complete
CMS-HCC	Aggregate model (Regression)	Inpatient + outpatient diagnoses	Calculation of individual risk scores	Medicare Part C, USA	Pope et al. [74]	Prospective	Yes	70	Hierarchical, additive weights	Incomplete
DCG/DCG-HCC/PIP-DCG	DCG: complete hierarchy as cell approach, DCG-HCC: Aggregate model (Regression)	DCG/HCC: Inpatient or outpatient and inpatient diagnoses	Risk-adjusted reimbursement/compensation systems for Medicare, Risk assessment, Efficiency audit of care providers, Calculation of premiums	Boston University, DxCG Inc., USA	Ash, Ellis, Pope et al. [65 72 73 74]	Prospective	Original-DCG: no DCH-HCC: yes PIP-DCG: no	DCG: 13 DCG-HCC: 132 PIP-DCG: 10	DCG: individual is assigned to most costly group HCC: hierarchical, additive cost weights	Complete	DCG: no
PCG+DCG	Full hierarchy as cell approach	Inpatient + outpatient diagnoses, Drug prescriptions, Age, Gender, Procedures, Region, Reason for insurance	Risk structure compensation system for statutory health insurance in the Netherlands	Erasmus University, Rotterdam, The Netherlands	Lamers & van Kliet [77 78]	Prospective	Yes	12	Original: only the most cost-intense PCG considered; Since 2008: Individual weights of diseases are additive	Incomplete	Yes
CD-RISC	Aggregate model (Regression)	Inpatient + outpatient diagnoses, Age, Gender, Medicaid- eligibility, degree of disability	Reimbursement/compensation, Development of risk-adjusted equalization payments,	Rand Cooperation, Santa Monica, CA, USA	Carter; Bell; Dubois [84 85]; Goldberg; Keeler; McAlearny; Post; Rumpel	Prospective	Yes	215	Hierarchical, Individual can be assigned to several hierarchies	Complete
CDPS- Rx	Aggregate model (Regression)	Inpatient + outpatient diagnoses for chronic diseases and disabilities, Age, Gender,	Reimbursement/compensation	University of California, San Diego, CA, USA	Gilmer, Kronick, Dreifuß, Lee [86 87]	Prospective	Yes	19	Considered, Individual can be assigned to more than one group	Complete
CRG/CRxG	Aggregate model (Regression)	Inpatient + outpatient diagnoses, Age, Gender, Procedures, Drug prescriptions	Development of risk-adjusted equalization payments, Monitoring	3M Health Information Systems, Wallingford, CT, USA; US Department of Commerce	Goldfield; Averill et al.	Prospective	Yes	1081	CRGs are based on combination of diseases, Individual is assigned to one of 9 health states	Complete
AAPCC	Cell approach	Age, Gender, Medicaid status, Nursing home status, Employment status + coverage	Compensation of Medicare + choice organizations	HCFA		Prospective	No	122	Not taken into account	Incomplete	No
ERG	Aggregate model (Regression)	Diagnoses, Age, Gender, Procedures, Drug prescriptions	Risk assessment, Efficiency audit of care providers, Calculation of premiums	Symmetry Health Data Systems, Inc., Phoenix, AZ, USA	Dunn et al.	Prospective	Yes	120	Individual can be assigned to more than one group	Complete
HMG	Aggregate model (Regression)	Age, Gender, Disability pensioner, Inpatient and outpatient diagnoses (rarely prescription)	Risk structure compensation system for statutory health insurance in Germany	German healthcare act (GKV-WSG)	German healthcare act (GKV-WSG)	Prospective	Yes	178	Individual can be assigned to more than one HMG	Incomplete
SQLape	Predominant diagnoses and procedures: Cell approach; Other diagnoses: Regression	Age, Gender, Inpatient Operations and diagnoses	Prognosis of hospital costs, Development of hospital quality indicators	University of Lausanne, CH	Yves Eggli [88]	Prospective	No	360, Mapping into 17 groups available	Considered, except for predominant diagnoses and operations	Complete	Yes, rough classification
GRAM	Aggregate model (Regression)	Diagnoses, Age, Gender	Prognosis of costs for Managed-Care beneficiaries	Kaiser Permanente, USA	Hornbrook, Fishman [75 89]	Prospective	Yes	118/93	Not considered, only highest-ranking diagnosis	Complete

German morbidity-oriented risk structure compensation scheme [104].

A Review on Methods of Risk Adjustment and their Use in Integrated Healthcare Systems

Figures & Tables

Figure 1

Table 1

Table 2

Figure 2

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