Diagnosis-based Risk Adjustment Research Articles

Comparison of diagnosis-based risk adjustment methods for episode-based costs to apply in efficiency measurement

BackgroundThe recent rising health spending intrigued efficiency and cost-based performance measures. However, mortality risk adjustment methods are still under consideration in cost estimation, though methods specific to cost estimate have been developed. Therefore, we aimed to compare the performance of diagnosis-based risk adjustment methods based on the episode-based cost to utilize in efficiency measurement.MethodsWe used the Health Insurance Review and Assessment Service–National Patient Sample as the data source. A separate linear regression model was constructed within each Major Diagnostic Category (MDC). Individual models included explanatory (demographics, insurance type, institutional type, Adjacent Diagnosis Related Group [ADRG], diagnosis-based risk adjustment methods) and response variables (episode-based costs). The following risk adjustment methods were used: Refined Diagnosis Related Group (RDRG), Charlson Comorbidity Index (CCI), National Health Insurance Service Hierarchical Condition Categories (NHIS-HCC), and Department of Health and Human Service-HCC (HHS-HCC). The model accuracy was compared using R-squared (R2), mean absolute error, and predictive ratio. For external validity, we used the 2017 dataset.ResultsThe model including RDRG improved the mean adjusted R2 from 40.8% to 45.8% compared to the adjacent DRG. RDRG was inferior to both HCCs (RDRG adjusted R2 45.8%, NHIS-HCC adjusted R2 46.3%, HHS-HCC adjusted R2 45.9%) but superior to CCI (adjusted R2 42.7%). Model performance varied depending on the MDC groups. While both HCCs had the highest explanatory power in 12 MDCs, including MDC P (Newborns), RDRG showed the highest adjusted R2 in 6 MDCs, such as MDC O (pregnancy, childbirth, and puerperium). The overall mean absolute errors were the lowest in the model with RDRG ($1,099). The predictive ratios showed similar patterns among the models regardless of the subgroups according to age, sex, insurance type, institutional type, and the upper and lower 10th percentiles of actual costs. External validity also showed a similar pattern in the model performance.ConclusionsOur research showed that either NHIS-HCC or HHS-HCC can be useful in adjusting comorbidities for episode-based costs in the process of efficiency measurement.

HPR130 Comparison of Diagnosis-Based Risk Adjustment Models for Episode-Based Cost

This study aims to compare the performance of diagnosis-based risk adjustment methods based on the episode-based cost to utilize in episode-based efficiency measurement.

Leveraging Existing Data from CMS-Linked Cohort Studies for the Advancement and Translation of Frailty Research

As population aging is accelerating rapidly, there is growing concern on how to best provide patient-centered care for the most vulnerable. Establishing a predictable and affordable cost structure for healthcare services is key to improving quality, accessibility, and affordability. One such effort is the “frailty” adjustment model implemented by the Centers for Medicare & Medicaid Services (CMS) that adjusts payments to a Medicare managed care organization based on functional impairment of its beneficiaries. Earlier studies demonstrated added value of this frailty adjuster for prediction of Medicare expenditures independent of the diagnosis-based risk adjustment. However, we hypothesize that further improvement is possible by implementing more rigorous frailty assessment rather than relying on self-report of ADL difficulties as used for the frailty adjuster. This is supported by the consensus and clinical observations that neither multimorbidity nor disability alone is sufficient for frailty identification. This symposium consists of four talks that leverage data from three CMS-linked cohort studies to investigate the utility of assessment of the frailty phenotype for predicting healthcare utilization and costs. Talk 1 and 2 use data from the NHATS cohort to assess healthcare utilization by frailty status in the general population and the homebound subset. Talk 3 and 4 use data from the MrOS study and the SOF study to investigate the impact of frailty phenotype on healthcare costs. Taken together, their findings highlight the potential of incorporating phenotypic frailty assessment into CMS risk adjustment to improve the planning and management of care for frail older adults.

Government Payments and Insurer Benefit Design in Medicare Part D

Subsidized health insurance markets use diagnosis-based risk adjustment to induce insurers to offer an equitable benefit to individuals of varying expected cost. I demonstrate that technological change after risk adjustment calibration -- new drug entry and the onset of generic competition -- made certain diagnoses profitable or unprofitable in Medicare Part D. I then exploit variation in diagnoses' profitability driven by technological change to show insurers designed more favorable benefits for drugs that treat profitable diagnoses as compared to unprofitable diagnoses. In the presence of technological change, risk adjustment may not fully neutralize insurers' incentives to select through benefit designs.

Technological Change and Risk Adjustment: Benefit Design Incentives in Medicare Part D

Subsidized health insurance markets use diagnosis-based risk adjustment to induce insurers to offer an equitable benefit to individuals of varying expected cost. I demonstrate that technological change after risk adjustment calibration—new drug entry and the onset of generic competition—made certain diagnoses profitable or unprofitable in Medicare Part D. I then exploit variation in diagnoses' profitability driven by technological change to show insurers designed more favorable benefits for drugs that treat profitable diagnoses as compared to unprofitable diagnoses. In the presence of technological change, risk adjustment may not fully neutralize insurers' incentives to select through benefit designs. (JEL G22, H51, I13, I18, L65, O33)

Honorarverteilung - sollte die morbiditätsbedingte Gesamtvergütung auch morbiditätsadjustiert verteilt werden?

In the 2009 reform of the German collective remuneration system for outpatient medical care, on the level of overall remuneration, the morbidity risk was transferred to the health funds fulfilling a long-term demand of physicians. Nevertheless not transferring morbidity adjustment to the levels of physician groups and singular practices can lead to budgets not related to patient needs and to incentives for risk selection for individual doctors. The systematics of the distribution of overall remuneration in the German remuneration system for outpatient care are analysed focusing on the aspect of morbidity adjustment. Using diagnostic and pharmaceutical information of about half a million insured subjects, a risk adjustment model able to predict individual expenditures for outpatient care for different provider groups is presented. This model enables to additively split the individual care burden into several parts attributed to different physician groups. Conditions for the use of the model in the distribution of overall remuneration between physician groups are developed. A simulation of the use of diagnoses-based risk adjustment in standard service volumes then highlights the conditions for a successfull installation of standard service volumes representing a higher degree of risk adjustment. The presented estimation model is generally applicable for the distribution of overall remuneration to different physician groups. The simulation of standard service volumes using diagnosis-based risk adjustment does not provide a more accurate prediction of the expenditures on the level of physician practices than the age-related calculation currently used in the German remuneration system for outpatient medical care. Using elements of morbidity-based risk adjustment the current German collective system for outpatient medical care could be transformed towards a higher degree of distributional justice concerning medical care for patients and more appropriate incentives avoiding risk selection. Limitations of the applicability of risk-adjustment can be especially pointed out when a high share of lump-sum-payments is used for the remuneration of some physician groups.

Risk adjustment in health insurance exchanges for individuals with mental illness.

In 2014, an estimated 15 million individuals who currently do not have health insurance, including many with chronic mental illness, are expected to obtain coverage through state insurance exchanges. The authors examined how two mechanisms in the Affordable Care Act (ACA), namely, risk adjustment and reinsurance, might perform to ensure the financial solvency of health plans that have a disproportionate share of enrollees with mental health conditions. Risk adjustment is an ACA provision requiring that a federal or state exchange move funds from insurance plans with healthier enrollees to plans with sicker enrollees. Reinsurance is a provision in which all plans in the state contribute to an overall pool of money that is used to reimburse costs to individual market plans for expenditures of any individual enrollee that exceed a high predetermined level. Using 2006--2007 claims data from a sample of private and public health plans, the authors compared expected health plan compensation under diagnosis-based risk adjustment with actual health care expenditures, under different assumptions for chronic mental health and medical conditions. Analyses were conducted with and without the addition of $100,000 reinsurance. Risk adjustment performed well for most plans. For some plans with a high share of enrollees with mental health conditions, underpayment was substantial enough to raise concern. Reinsurance appeared to be helpful in addressing the most serious underpayment problems remaining after risk adjustment. Risk adjustment performed similarly for health plan cohorts that had a disproportionate share of enrollees with chronic mental health and medical conditions. Cost models indicate that the regulatory provisions in the ACA requiring risk adjustment and reinsurance can help protect health plans covering treatment for mentally ill individuals against risk selection. This model analysis may be useful for advocates for individuals with mental illness in considering their own state's insurance exchange.

Setting health care capitations through diagnosis-based risk adjustment: A suitable model for the English NHS?

The English system of health resource allocation has been described as the apotheosis of the area-level approach to setting health care capitations. However, recent policy developments have changed the scale at which commissioning decisions are made (and budgets allocated) with important implications for resource allocation. Doubts concerning the legitimacy of applying area-based formulae used to distribute resources between Primary Care Trusts (PCTs) to the much smaller scale required by Practice Based Commissioning (PBC) led the English Department of Health (DH) to introduce a new approach to setting health care budgets. To this end, practice-level allocations for acute services are now calculated using a diagnosis-based capitation model of the kind used in the United States and several other systems of competitive social health insurance. The new Coalition Government has proposed that these budgets are directly allocated to GP ‘consortia’, the new commissioning bodies in the NHS. This paper questions whether this is an appropriate development for a health system in which the major objective of resource allocation is to promote equal opportunity of access for equal needs. The chief reservation raised is that of circularity and the perpetuation of resource bias, the concern being that an existing social, demographic and geographical bias in the use of health care resources will be reinforced through the use of historic utilisation data. Demonstrating that there are legitimate reasons to suspect that this will be the case, the paper poses the question whether health systems internationally should more openly address the key limitations of empirical methods that select risk adjusters on the basis of existing patterns of health service utilisation.

An in-depth assessment of a diagnosis-based risk adjustment model based on national health insurance claims: the application of the Johns Hopkins Adjusted Clinical Group case-mix system in Taiwan

BackgroundDiagnosis-based risk adjustment is becoming an important issue globally as a result of its implications for payment, high-risk predictive modelling and provider performance assessment. The Taiwanese National Health Insurance (NHI) programme provides universal coverage and maintains a single national computerized claims database, which enables the application of diagnosis-based risk adjustment. However, research regarding risk adjustment is limited. This study aims to examine the performance of the Adjusted Clinical Group (ACG) case-mix system using claims-based diagnosis information from the Taiwanese NHI programme.MethodsA random sample of NHI enrollees was selected. Those continuously enrolled in 2002 were included for concurrent analyses (n = 173,234), while those in both 2002 and 2003 were included for prospective analyses (n = 164,562). Health status measures derived from 2002 diagnoses were used to explain the 2002 and 2003 health expenditure. A multivariate linear regression model was adopted after comparing the performance of seven different statistical models. Split-validation was performed in order to avoid overfitting. The performance measures were adjusted R2 and mean absolute prediction error of five types of expenditure at individual level, and predictive ratio of total expenditure at group level.ResultsThe more comprehensive models performed better when used for explaining resource utilization. Adjusted R2 of total expenditure in concurrent/prospective analyses were 4.2%/4.4% in the demographic model, 15%/10% in the ACGs or ADGs (Aggregated Diagnosis Group) model, and 40%/22% in the models containing EDCs (Expanded Diagnosis Cluster). When predicting expenditure for groups based on expenditure quintiles, all models underpredicted the highest expenditure group and overpredicted the four other groups. For groups based on morbidity burden, the ACGs model had the best performance overall.ConclusionsGiven the widespread availability of claims data and the superior explanatory power of claims-based risk adjustment models over demographics-only models, Taiwan's government should consider using claims-based models for policy-relevant applications. The performance of the ACG case-mix system in Taiwan was comparable to that found in other countries. This suggested that the ACG system could be applied to Taiwan's NHI even though it was originally developed in the USA. Many of the findings in this paper are likely to be relevant to other diagnosis-based risk adjustment methodologies.

Improving Risk Adjustment of Self-Reported Mental Health Outcomes

Risk adjustment for mental health care is important for making meaningful comparisons of provider, program, and system performance. The purpose of this study was to compare the predictive value of three diagnosis-based risk-adjustment models for predicting self-reported mental health outcomes. Baseline and 3-month follow-up mental health assessments were obtained on 1,023 veterans in Veterans Health Administration mental health programs between 2004 and 2006. Least-squares regression models predicting mental health outcomes used the Behavior and Symptom Identification Scale-24, Veterans RAND-36, and Brief Symptom Inventory. Sequential models began with sociodemographics, added baseline self-reported mental health, and compared three psychiatric case mix schemes: two using six diagnostic categories and the other (psychiatric case mix system [PsyCMS]) using 46 categories. R (2) were lowest for sociodemographic models (0.010-0.074) and highest for models with the PsyCMS (0.187-0.425). The best predictive ability was obtained when baseline mental health and 1 year of psychiatric diagnoses were added to sociodemographic models; however, the "best" risk-adjustment model differed between inpatients and outpatients.

How well does diagnosis-based risk-adjustment work for comparing ambulatory clinical outcomes?

This paper examines the empirical consistency of the Diagnosis Cost Groups/Hierarchical Condition Categories (DCG/HCC) risk-adjustment method for comparing 7-day mortality between hospital-based outpatient departments (HOPDs) and freestanding ambulatory surgery centers (ASCs). We used patient level data for the three most common outpatient procedures provided during the 1997-2004 period in Florida. We estimated base-line logistic regression models without any diagnosis-based risk adjustment and compared them to logistic regression models with the DCG/HCC risk-adjustment, and to conditional logit models with a matched cohort risk-adjustment approach. We also evaluated models that adjusted for primary diagnoses only, and then for all available diagnoses, to assess how the frequently absent secondary diagnoses fields in ambulatory surgical data affect risk-adjustment. We found that risk-adjustment using both diagnosis-based methods resulted in similar 7-day mortality estimates for HOPD patients in comparison with ASC patients in two out of three procedures. We conclude that the DCG/HCC risk-adjustment method is relatively consistent and stable, and recommend this risk-adjustment method for health policy research and practice with ambulatory surgery data. We also recommend using risk-adjustment with all available diagnoses.

Analysis of predictive value of four risk models in medicaid recipients with chronic obstructive pulmonary disease in Texas

Objective: The aim of this study was to assess the predictive value of 4 risk models when applied to Medicaid recipients with chronic obstructive pulmonary disease (COPD) in Texas. Methods: Four risk models were used to predict future medical costs: Diagnostic Cost Group (DCG) Inpatient + RxGroups Commercial, DCG All-Encounter + RxGroups Commercial, DCG All-Encounter + RxCost Medicaid, and prior cost. Diagnostic codes, National Drug Code categories, and 2001 total health care costs were input into the risk models. The study included Texas Medicaid recipients aged 40 to <65 years who had an inpatient diagnosis of COPD in 2000 and were continuously enrolled in Medicaid in 2000–2002. The risk models were used to predict total health care costs for 2002. Predicted costs were compared with actual 2002 costs using R 2, and predictions of high-cost cases were analyzed using the positive predictive value (PPV) of the top 5% of high-cost patients. Results: Information from 7967 patients was used in this analysis. The R 2 values were 0.11, 0.08, 0.05, and 0.04 with the DCG Inpatient + RxGroups Commercial, DCG All-Encounter + RxGroups Commercial, DCG All-Encounter + RxCost Medicaid, and prior cost models, respectively. In the top 5% of high-cost patients, the corresponding PPVs were 26% (105/399), 23% (90/399), 18% (72/399), and 28% (112/399). Conclusions: The DCG Inpatient + RxGroups Commercial model was the best predictor of future costs as measured using R 2. However, diagnosis-based risk-adjustment models may not be as useful as prior cost data when attempting to identify future high-cost cases among Texas Medicaid recipients with COPD. A combination of diagnosis-based risk adjustment models and prior cost data may prove to be the most effective way to use claims data to identify COPD candidates for case management.

Use of a diagnosis-based risk adjustment model to estimate costs of indigent care in a community at Medicaid reimbursement rates

Objectives: This study used a diagnosis-based risk adjustment model to estimate the annual costs of uninsured patients in Austin, Texas, and describe the prevalence and costs of their chronic conditions. The data were supplied by the Indigent Care Collaboration, a partnership of local safety-net hospitals and clinics.Methods: This study used the Diagnostic Cost Groups prospective Medicaid All-Encounters model, which uses diagnoses, age and gender to assign relative risk scores to patients. The relative risk scores were multiplied by the per capita Texas Medicaid expenditure to obtain estimated annual costs. Chronic diseases were described in terms of prevalence and total estimated annual cost.Results: A total of 471,194 encounters were recorded for 163,729 patients meeting the study inclusion criteria between the 1st March 2004 and the 28th February 2005. The mean estimated patient yearly cost was US $1,307, and the total estimated yearly population cost was $228,909,529. The most common chronic conditions included hypertension, diabetes, depression, substance abuse, pregnancy, asthma, chronic obstructive pulmonary disease and congestive heart failure.Conclusions: This study demonstrates how the unknown costs associated with caring for indigent uninsured patients in a community can be estimated at Medicaid reimbursement rates using the Diagnostic Cost Group model on aggregated patient encounter data.

PMC5 USE OF A DIAGNOSIS-BASED RISK ADJUSTMENT MODEL TO ESTIMATE COSTS OF INDIGENT CARE IN A COMMUNITY AT MEDICAID REIMBURSEMENT RATES

Objectives: This study used a diagnosis-based risk adjustment model to estimate the annual costs of uninsured patients in Austin, Texas, and describe the prevalence and costs of their chronic condi...

The Effect of Referral and Transfer Patients on Hospital Funding in a Capitated Health Care Delivery System

(1) To investigate whether inpatients referred or transferred between facilities result in increased financial loss compared with those admitted directly, in a health care delivery system funded by capitation methods. (2) To determine whether the higher cost of those patients transferred or referred is fairly compensated by a diagnosis-based risk adjustment system, and whether tertiary care facilities bear an unfair financial burden for such patients in a capitated financing environment. The study cohort included all Veterans Affairs (VA) beneficiaries who received inpatient care during fiscal year (FY) 2004. Referral was defined as an outpatient visit to 1 facility followed by an admission to another facility. Transfers were consecutive inpatient stays at different hospitals. We defined loss as cost minus the share of budget determined by a Diagnostic Cost Group-based allocation. Both t tests and linear regression were used to compare the effect on cost and loss for patients transferred or not and referred or not. Mean loss to a facility for patients transferred in was 1231 dollars more than for those not transferred. Mean loss for referred patients was 3341 dollars more than for those not referred, controlling for disease burden. For tertiary hospitals, the difference in losses for transfer patients was less than for other hospitals but greater for referral patients. Patients referred or transferred from other facilities are more costly than those who are not. The difference may not be compensated by a diagnosis-based allocation system. A capitated health care system may consider additional funding to cover the cost of such patients.

Comparing Self‐Reported Health Status and Diagnosis‐Based Risk Adjustment to Predict 1‐ and 2 to 5‐Year Mortality

To compare the ability of two diagnosis-based risk adjustment systems and health self-report to predict short- and long-term mortality. Data were obtained from the Department of Veterans Affairs (VA) administrative databases. The study population was 78,164 VA beneficiaries at eight medical centers during fiscal year (FY) 1998, 35,337 of whom completed an 36-Item Short Form Health Survey for veterans (SF-36V) survey. We tested the ability of Diagnostic Cost Groups (DCGs), Adjusted Clinical Groups (ACGs), SF-36V Physical Component score (PCS) and Mental Component Score (MCS), and eight SF-36V scales to predict 1- and 2-5 year all-cause mortality. The additional predictive value of adding PCS and MCS to ACGs and DCGs was also evaluated. Logistic regression models were compared using Akaike's information criterion, the c-statistic, and the Hosmer-Lemeshow test. The c-statistics for the eight scales combined with age and gender were 0.766 for 1-year mortality and 0.771 for 2-5-year mortality. For DCGs with age and gender the c-statistics for 1- and 2-5-year mortality were 0.778 and 0.771, respectively. Adding PCS and MCS to the DCG model increased the c-statistics to 0.798 for 1-year and 0.784 for 2-5-year mortality. The DCG model showed slightly better performance than the eight-scale model in predicting 1-year mortality, but the two models showed similar performance for 2-5-year mortality. Health self-report may add health risk information in addition to age, gender, and diagnosis for predicting longer-term mortality.

Diagnosis-based risk adjustment and Australian health system policy

Diagnosis-based risk adjustment is increasingly seen as an important tool for establishing capitation payments and evaluating appropriateness and efficiency of services provided and has become an important area of research for many countries contemplating health system reform. This paper examines the application of a risk-adjustment method, extensively validated in the United States, known as diagnostic cost groups (DCG), to a large Australian hospital inpatient data set. The data set encompassed hospital inpatient diagnoses and inpatient expenditure for the entire metropolitan population residing in the state of New South Wales. The DCG model was able to explain 34% of individual-level variation in concurrent expenditure and 5.2% in subsequent year expenditure, which is comparable to US studies using inpatient-only data. The degree of stability and internal consistency of the parameter estimates for both the concurrent and prospective models indicate the DCG methodology has face validity in its application to NSW health data sets. Modelling and simulations were conducted which demonstrate the policy applications and significance of risk adjustment model(s) in the Australian context. This study demonstrates the feasibility of using large individual-level data sets for diagnosis-based risk adjustment research in Australia. The results suggest that a research agenda should be established to broaden the options for health system reform.

Use of Diagnosis-Based Risk Adjustment Models to Predict Individual Health Care Expenditure under the National Health Insurance System in Taiwan

Diagnostic information has been extensively studied and employed in the prediction of risk adjusted capitation payments in some countries. Nevertheless, few studies have been dedicated to the development of diagnosis-based risk adjusters in Taiwan. The purposes of this study were to develop outpatient diagnosis-based risk adjusters for a model of Taiwan's National Health Insurance (NHI) system and to evaluate the predictability of the risk adjustment models generated utilizing these adjusters. Using a 2% random sample of 371,620 NHI enrollees, 5 risk adjustment models--i.e., demographic, inpatient diagnostic information outpatient diagnostic information, full diagnostic information, and prior utilization models--were evaluated with respect to predictive R2 and predictive ratios. While inpatient diagnosis-based risk adjusters were borrowed from previous research, outpatient diagnosis-based risk adjusters, referred to as Taiwan Ambulatory Spending Groups (TASGs), were developed based on 1996 claims data. The values of predictive R2 for the 5 risk adjustment models showed that the inclusion of outpatient diagnostic information considerably improved the predictability of the risk adjustment models for Taiwan's NHI system. Moreover, the predictive ratios revealed that the full diagnostic information model would reimburse different risk subgroups more fairly than the demographic, inpatient diagnostic information, and outpatient diagnostic information models and also outperform the prior utilization model with respect to disease risk groups. The risk adjustment model including the TASG risk adjusters can significantly improve predictability and can be employed to assess the NHI's current and proposed reform measures.

Pharmacy- and Diagnosis-Based Risk Adjustment for Children With Medicaid

Risk adjustment is useful for adjusting health care payments based on patients' health status. This work seeks to examine how well pharmacy- and diagnosis-based risk adjusters predict child health expenditures in Medicaid populations. We used 1994-1995 Medicaid claims files for all children ages 0-18 years who were not covered by managed care in 3 states: Georgia, New Jersey, and Wisconsin. We examined separately 6 risk adjustment methods, 2 pharmacy-based and 4 diagnosis-based. We compared predictive accuracy of the methods for the whole sample and stratified by state and Medicaid enrollment category. Models with risk adjustment (either diagnosis- or pharmacy-based) had better predictive accuracy than demographic models. The pharmacy and diagnosis-based models had similar predictive accuracy. Risk adjuster performance differed by Medicaid enrollment category and state. Risk-adjusted models generally underpredict expenditures in populations with worse health status (eg, those in the Supplemental Security Income program [SSI]). The pharmacy-based models performed well for children in SSI relative to children in foster care. Both pharmacy- and diagnosis-based risk adjustment improved the prediction of health expenditures compared models without risk adjustment. No single risk adjuster performed best in all situations, suggesting that optimal choices of risk adjusters may differ by purpose and context.

Identifying Future High-Healthcare Users

Diagnosis-based risk-adjustment measures are increasingly being promoted as disease management tools. We compared the ability of several types of predictive models to identify future high-risk older people likely to benefit from disease management. Veterans Health Administration (VHA) data were used to identify veterans ≥65 years of age who used healthcare services during fiscal years (FY) 1997 and 1998 and who remained alive through FY 1997. This yielded a development sample of 412 679 individuals and a validation sample of 207 294. Prospective risk-adjustment models were fitted and tested using Adjusted Clinical Groups (ACGs), Diagnostic Cost Groups (DCGs), a prior-utilization model (prior), and combined models (prior + ACGs and prior + DCGs). Prespecified high use in FY 1998 was defined as ≥92 days of care (top 2.2%) for an individual (i.e. the number of days during the year in which an individual received inpatient or outpatient healthcare services). We developed a second outcome, defined as ≥164 days of care (top 1.0%), to explore whether changing the criterion for high risk would affect the number of misclassifications. The diagnosis-based models performed better than the prior model in identifying a subgroup of future high-cost individuals with high disease burden and chronic diseases appropriate for disease management. The combined models performed best at correctly classifying those without high use in the prospective year. The utility for efficiently identifying high-risk cases appeared limited because of the high number of individuals misclassified as future high-risk cases by all the models. Changing the criterion for high risk generally decreased the number of patients misclassified. There was little agreement between the models regarding who was identified as high risk. Health plans should be aware that different risk-adjustment measures may select dissimilar groups of individuals for disease management. Although diagnosis-based measures show potential as predictive modeling tools, combining a diagnosis-based measure with prior-utilization model may yield the best results.