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At least 19 recordsLinked to original sources
PubMed2003

Risk adjustment and risk selection on the sickness fund insurance market in five European countries.

From the mid-1990s citizens in Belgium, Germany, Israel, the Netherlands and Switzerland have a guaranteed periodic choice among risk-bearing sickness funds, who are responsible for purchasing their care or providing them with medical care. The rationale of this arrangement is to stimulate the sickness funds to improve efficiency in health care production and to respond to consumers' preferences. To achieve solidarity, all five countries have implemented a system of risk-adjusted premium subsidies (or risk equalization across risk groups), along with strict regulation of the consumers' direct premium contribution to their sickness fund. In this article we present a conceptual framework for understanding risk adjustment and comparing the systems in the five countries. We conclude that in the case of imperfect risk adjustment-as is the case in all five countries in the year 2001-the sickness funds have financial incentives for risk selection, which may threaten solidarity, efficiency, quality of care and consumer satisfaction. We expect that without substantial improvements in the risk adjustment formulae, risk selection will increase in all five countries. The issue is particularly serious in Germany and Switzerland. We strongly recommend therefore that policy makers in the five countries give top priority to the improvement of the system of risk adjustment. That would enhance solidarity, cost-control, efficiency and client satisfaction in a system of competing, risk-bearing sickness funds.

Capitation Fee↗
PubMed2003

Risk adjustment and risk sharing: the Israeli experience.

Israel, like several other countries, introduced a national risk adjusted capitation system during the 1990s. However, the Israeli move was drastic, implementing from the beginning a fully prospective risk adjustment scheme based on age, supplemented by a 100% five condition-specific risk sharing. That scheme, together with open enrollment (periodic switching options), was intended to transform an unregulated competitive health insurance market, characterized by adverse selection and preferred risk selection, into managed competition assuring quality of care, efficiency and fairness. This paper presents the Israeli experience during the first 6 years of the reformed system, focusing on issues related to the risk adjustment and risk sharing arrangements.

Adolescent↗
PubMed1998

Risk adjustment: where are we now?

Risk adjustment is intended to minimize selection of patients or enrollees in health plans. Current efforts generally are recognized as inadequate, but improvement is difficult. The greatest short-term gain will come from introducing diagnostic information, though outpatient diagnosis data are unreliable. Initial efforts may use inpatient data, but this creates incentives to hospitalize people. Even exploiting diagnosis information leaves substantial imperfections. Partial capitation, common in behavioral health, reduces incentives to select patients and stent on services, but current policy resists it, perhaps because policymakers misinterpret the lesson of the Prospective Payment System. Theoretically, not paying plans more for providing additional services is optimal only if consumers are well informed.

Capitation Fee↗
PubMed2006

Direct comparison of risk-adjusted and non-risk-adjusted CUSUM analyses of coronary artery bypass surgery outcomes.

OBJECTIVE: We previously applied non-risk-adjusted cumulative sum methods to analyze coronary bypass outcomes. The objective of this study was to assess the incremental advantage of risk-adjusted cumulative sum methods in this setting. METHODS: Prospective data were collected in 793 consecutive patients who underwent coronary bypass grafting performed by a single surgeon during a period of 5 years. The composite occurrence of an "adverse outcome" included mortality or any of 10 major complications. An institutional logistic regression model for adverse outcome was developed by using 2608 contemporaneous patients undergoing coronary bypass. The predicted risk of adverse outcome in each of the surgeon's 793 patients was then calculated. A risk-adjusted cumulative sum curve was then generated after specifying control limits and odds ratio. This risk-adjusted curve was compared with the non-risk-adjusted cumulative sum curve, and the clinical significance of this difference was assessed. RESULTS: The surgeon's adverse outcome rate was 96 of 793 (12.1%) versus 270 of 1815 (14.9%) for all the other institution's surgeons combined (P = .06). The non-risk-adjusted curve reached below the lower control limit, signifying excellent outcomes between cases 164 and 313, 323 and 407, and 667 and 793, but transgressed the upper limit between cases 461 and 478. The risk-adjusted cumulative sum curve never transgressed the upper control limit, signifying that cases preceding and including 461 to 478 were at an increased predicted risk. Furthermore, if the risk-adjusted cumulative sum curve was reset to zero whenever a control limit was reached, it still signaled a decrease in adverse outcome at 166, 653, and 782 cases. CONCLUSIONS: Risk-adjusted cumulative sum techniques provide incremental advantages over non-risk-adjusted methods by not signaling a decrement in performance when preoperative patient risk is high.

Adult↗
PubMed2005

Quality improvement in interventional cardiology: the role of risk adjustment in evaluation of health outcomes.

BACKGROUND: The use of the methodology of adjusted risk to check the calculation of the differences between patients, groups and populations, as regards outcomes, is being used more often in the era of modern interventional cardiology to validate a reliable and balanced comparison of results between institutions and to maintain uniformity of data, criteria and definitions with a view to carrying out multicenter studies in this area of medicine. In this way, the ability to determine which variables have greater predictive value for adverse events that result from percutaneous coronary intervention (PCI) is an extremely important tool for clinical decisions and for risk adjustment in each patient, when evaluating the quality of the health care given. METHODS AND RESULTS: this work took the research design of a case-control study. The data analyzed related to all patients who had undergone PCI in an interventional cardiology unit, during 2002 (567 patients). The group of cases (33) corresponds to those patients in whom a major adverse cardiac or cerebrovascular event (MACCE) occurred during the procedure or within a 30-day period. The control group (534 patients) was made up of the rest of the population that underwent a PCI but that remained free of events in the same study period. The data on the PCIs carried out was taken from the database of the interventional cardiology unit. Analysis of the data was based on a descriptive and analytical statistical treatment (SPSS 11.5) with recourse to contingency tables, the Pearson's chi-square test, the Fisher exact test, and simple and multiple logistic regression, using odds ratios (OR) as a measure of association. The level of significance was 0.05 and the confidence interval was 95%. It should be noted that in the group of variables that showed greatest predictive value of the occurrence of an MACCE we found the following: female gender (OR = 1.593), advanced age (80 years, OR = 9.460), diabetes (OR = 3.063), chronic renal failure (OR = 3.063), depressed ejection fraction (30%, OR = 8.475), priority at PCI (OR = 7.108), multivessel disease (OR = 1.683), type C lesion (OR = 2.208), acute myocardial infarction (OR = 5.045) and cardiogenic shock (OR = 28.169). CONCLUSIONS: This study allowed us to identify, characterize and grade the set of variables that presented the strongest association with major adverse events that resulted from PCIs, for the population that underwent this type of procedure in an interventional coronary unit over the course of one year. At the same time, it enabled us to characterize the procedures that were carried out during this period, and also the demographic and clinical profile of the population. Although in some of the variables the OR values found were not statistically significant in either bivariate or multivariate analysis, we should point out that these variables are comparable to most results from similar multicenter international studies carried out with large populations. This type of study is an important contribution to improving the quality of health care given and to more efficient risk management in the field of interventional cardiology.

Angioplasty, Balloon, Coronary↗
PubMed1997

The risks of risk adjustment.

CONTEXT: Risk adjustment is essential before comparing patient outcomes across hospitals. Hospital report cards around the country use different risk adjustment methods. OBJECTIVES: To examine the history and current practices of risk adjusting hospital death rates and consider the implications for using risk-adjusted mortality comparisons to assess quality. DATA SOURCES AND STUDY SELECTION: This article examines severity measures used in states and regions to produce comparisons of risk-adjusted hospital death rates. Detailed results are presented from a study comparing current commercial severity measures using a single database. It included adults admitted for acute myocardial infarction (n=11880), coronary artery bypass graft surgery (n=7765), pneumonia (n=18016), and stroke (n=9407). Logistic regressions within each condition predicted in-hospital death using severity scores. Odds ratios for in-hospital death were compared across pairs of severity measures. For each hospital, z scores compared actual and expected death rates. RESULTS: The severity measure called Disease Staging had the highest c statistic (which measures how well a severity measure discriminates between patients who lived and those who died) for acute myocardial infarction, 0.86; the measure called All Patient Refined Diagnosis Related Groups had the highest for coronary artery bypass graft surgery, 0.83; and the measure, MedisGroups, had the highest for pneumonia, 0.85 and stroke, 0.87. Different severity measures predicted different probabilities of death for many patients. Severity measures frequently disagreed about which hospitals had particularly low or high z scores. Agreement in identifying low- and high-mortality hospitals between severity-adjusted and unadjusted death rates was often better than agreement between severity measures. CONCLUSIONS: Severity does not explain differences in death rates across hospitals. Different severity measures frequently produce different impressions about relative hospital performance. Severity-adjusted mortality rates alone are unlikely to isolate quality differences across hospitals.

Benchmarking↗
PubMed2002

Cost-minimizing risk adjustment.

Conventional risk adjustment, which sets capitation payments equal to the average cost of individuals with similar observable characteristics, is not optimal if health plans can use private information to select low-cost enrollees. "Cost-minimizing risk adjustment" minimizes the sum of capitated HMO premiums plus FFS costs by balancing the gains from HMO cost efficiency against the overpayments that result from HMO selection. Estimations using privately-insured data suggest that cost-minimizing risk adjusted premiums reduce total sponsor costs as much as 25.6% below conventional risk adjustment premiums.

Actuarial Analysis↗
PubMed2003

Risk adjusted premium subsidies and risk sharing: key elements of the competitive sickness fund market in the Netherlands.

As part of a market-oriented health care reform, in 1991 risk adjusted premium subsidies were introduced in the Dutch social health insurance sector. Currently the premium subsidies are primarily based on demographic variables. To mitigate the obvious inadequacy of these risk adjusters, the system of risk adjustment is supplemented with a system of risk sharing. This paper describes the main characteristics of the Dutch health care system and the development of risk adjustment and risk sharing in the last decade. The effects of introducing financial risk for Dutch sickness funds on risk selection and consumer mobility are analysed. The paper concludes with a description of expected future developments.

Capitation Fee↗
PubMed1994

Using risk-adjusted outcomes to assess clinical practice: an overview of issues pertaining to risk adjustment.

Increasingly, health care providers are being evaluated and held accountable for their patients' outcomes, ranging from the costs to service consumption to death. To be meaningful, the outcomes under scrutiny must be important to patients or to the health care system as a whole, relatively common, and linked temporally and causally to the care provided. In addition, outcomes findings should be adjusted for patient risk factors, with the goal of accounting for pertinent clinical characteristics before drawing inferences about the effectiveness or quality of care. Risk adjustment "levels the playing field" in comparing outcomes across providers. Although this concept is straightforward, performing clinically credible risk adjustment is difficult, especially given the widespread data constraints. In this article, I review the major issues involved in performing risk adjustment for health care outcomes studies.

Health Services Research↗
PubMed2006

Predicting future healthcare costs: how well does risk-adjustment work?

PURPOSE: Risk-adjustment is designed to predict healthcare costs to align capitated payments with an individual's expected healthcare costs. This can have the consequence of reducing overpayments and incentives to under treat or reject high cost individuals. This paper seeks to review recent studies presenting risk-adjustment models. DESIGN/METHODOLOGY/APPROACH: This paper presents a brief discussion of two commonly reported statistics used for evaluating the accuracy of risk adjustment models and concludes with recommendations for increasing the predictive accuracy and usefulness of risk-adjustment models in the context of predicting future healthcare costs. FINDINGS: Over the last decade, many advances in risk-adjustment methodology have been made. There has been a focus on the part of researchers to transition away from including only demographic data in their risk-adjustment models to incorporating patient data that are more predictive of healthcare costs. This transition has resulted in more accurate risk-adjustment models and models that can better identify high cost patients with chronic medical conditions. ORIGINALITY/VALUE: The paper shows that the transition has resulted in more accurate risk-adjustment models and models that can better identify high cost patients with chronic medical conditions.

Capitation Fee↗
PubMed2002

Performance of diagnosis-based risk adjustment measures in a population of sick Australians.

OBJECTIVE: Australia is beginning to explore 'managed competition' as an organising framework for the health care system. This requires setting fair capitation rates, i.e. rates that adjust for the risk profile of covered lives. This paper tests two US-developed risk adjustment approaches using Australian data. METHODS: Data from the 'co-ordinated care' dataset (which incorporates all service costs of 16,538 participants in a large health service research project conducted in 1996-99) were grouped into homogenous risk categories using risk adjustment 'grouper software'. The grouper products yielded three sets of homogenous categories: Diagnostic Groups and Diagnostic cost Groups. A two-stage analysis of predictive power was used: probability of any service use in the concurrent year, next year and the year after (logistic regression) and, for service users, a regression of logged cost of service use. The independent variables were diagnosis gender, a SES variable and the RESULTS: Age, gender and diagnosis-based risk adjustment measures explain around 40-45% of variation in costs of service use in the current year for untrimmed data (compared with around 15% for age and gender alone). Prediction of subsequent use is much poorer (around 20%). Using more information to assign people to risk categories generally improves prediction. CONCLUSIONS: Predictive power of diagnosis-base risk adjusters on this Australian dataset is similar to that found in IMPLICATIONS: Low predictive power carries policy risks of cream skimming rather than managing population health and care. Competitive funding models with risk adjustment on prior year experience could reduce system efficiency if implemented with current risk adjustment technology.

Adolescent↗
PubMed2006

Should episode-based economic profiles be risk adjusted to account for differences in patients' health risks?

OBJECTIVE: To determine whether additional risk adjustment is necessary in economic profiling of physicians when claims data are already grouped into episodes of care, and to measure effects of risk adjustment on cost efficiency rankings of physicians. DATA SOURCES: Four years of inpatient, outpatient, professional, and pharmacy claims data from a mixed model HMO. STUDY DESIGN: Claims data were processed through Symmetry Health Data Systems' episode treatment group (ETG) grouper to define episodes of care and Symmetry's episode risk group (ERG) software to define measures of patients' health risk scores. For each episode type (ETG), ETG-mean expected costs were calculated as the mean costs of all episodes of that type, and risk-adjusted expected costs were calculated using three alternative risk model formulations. DATA COLLECTION: Within specialties, physicians were ranked from most cost efficient to least cost efficient, based on standardized difference between actual and expected costs. ETG-mean based rankings were compared with risk-adjusted rankings. Analyses were performed for cardiologists, family practitioners, general surgeons, and neurologists. PRINCIPAL FINDINGS: With all three risk models, risk scores were essentially unrelated to episode costs in approximately three-fourths of episode categories (ETGs). In a sample of ETGs for which risks-costs relationships appeared to exist, split sample validation showed the relationships to be unstable or spurious in all except one ETG. Within specialties, risk-adjusted cost efficiency rankings differ little from ETG-mean adjusted rankings. CONCLUSIONS: Depending upon the purpose for which economic profiling is performed, additional risk adjustment, beyond that already provided by episode grouping, may be unnecessary. Additional research may be needed to identify and validate ETG-level relationships between patient risks and episode costs.

Cost-Benefit Analysis↗
PubMed2003

Using risk-adjustment models to identify high-cost risks.

BACKGROUND: We examine the ability of various publicly available risk models to identify high-cost individuals and enrollee groups using multi-HMO administrative data. METHODS: Five risk-adjustment models (the Global Risk-Adjustment Model [GRAM], Diagnostic Cost Groups [DCGs], Adjusted Clinical Groups [ACGs], RxRisk, and Prior-expense) were estimated on a multi-HMO administrative data set of 1.5 million individual-level observations for 1995-1996. Models produced distributions of individual-level annual expense forecasts for comparison to actual values. Prespecified "high-cost" thresholds were set within each distribution. The area under the receiver operating characteristic curve (AUC) for "high-cost" prevalences of 1% and 0.5% was calculated, as was the proportion of "high-cost" dollars correctly identified. Results are based on a separate 106,000-observation validation dataset. MAIN RESULTS: For "high-cost" prevalence targets of 1% and 0.5%, ACGs, DCGs, GRAM, and Prior-expense are very comparable in overall discrimination (AUCs, 0.83-0.86). Given a 0.5% prevalence target and a 0.5% prediction threshold, DCGs, GRAM, and Prior-expense captured $963,000 (approximately 3%) more "high-cost" sample dollars than other models. DCGs captured the most "high-cost" dollars among enrollees with asthma, diabetes, and depression; predictive performance among demographic groups (Medicaid members, members over 64, and children under 13) varied across models. CONCLUSIONS: Risk models can efficiently identify enrollees who are likely to generate future high costs and who could benefit from case management. The dollar value of improved prediction performance of the most accurate risk models should be meaningful to decision-makers and encourage their broader use for identifying high costs.

Adolescent↗
PubMed2001

The Medicaid Rx model: pharmacy-based risk adjustment for public programs.

BACKGROUND: Risk adjustment models typically use diagnoses from claims or encounter records to assess illness severity. However, concerns about the availability and reliability of diagnostic data raise the potential for alternative methods of risk adjustment. Here, we explore the use of pharmacy data as an alternative or complement to diagnostic data in risk adjustment. OBJECTIVES: To develop and test a pharmacy-based risk adjustment model for SSI and TANF Medicaid populations. RESEARCH DESIGN: Pharmacological review combined with empirical evaluation. We developed the Medicaid Rx model, a system that classifies a subset of the National Drug Codes into categories that can be used for risk-assessment and risk-adjusted payment. SUBJECTS: Subjects consisted of 362,370 persons with disability and 1.5 million AFDC and TANF beneficiaries in California, Colorado, Georgia, and Tennessee during 1990-1999. MEASURES: We compare pharmacy and diagnostic classification for three chronic diseases. We also compare R2 statistics and use simulated health plans to evaluate the performance of alternative models. RESULTS: Pharmacy and diagnostic classification vary in their ability to identify specific chronic disease. Using simulated plans, diagnostic models are better at predicting expenditures than are pharmacy-based models for disabled Medicaid beneficiaries, although the models perform similarly for TANF Medicaid beneficiaries. Models that combine diagnostic and pharmacy data have superior overall performance. CONCLUSIONS: The performance of risk adjustment models using a combination of pharmacy and diagnostic data are superior to that of models using either data source alone, particularly among TANF beneficiaries. Concerns regarding variations in prescribing patterns and the incentives that may follow from linking payment to pharmacy use warrant further research.

Adult↗
PubMed2005

Pharmacy-and diagnosis-based risk adjustment for children with Medicaid.

BACKGROUND: Risk adjustment is useful for adjusting health care payments based on patients' health status. OBJECTIVE: This work seeks to examine how well pharmacy- and diagnosis-based risk adjusters predict child health expenditures in Medicaid populations. RESEARCH DESIGN: 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. FINDINGS: 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. CONCLUSIONS: 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.

Adolescent↗
PubMed2004

Risk adjusting rehabilitation outcomes: an overview of methodologic issues.

The complexity and mix of rehabilitation patients varies across clinicians and institutions. Comparisons of outcomes across providers must therefore adjust for differences in risk factors across patient populations. Research on risk adjustment has generally focused on acute care hospital outcomes, although techniques for risk adjusting financial outcomes are fairly well developed in rehabilitation, primarily to support Medicare and other prospective payment systems. This article reviews important methodologic issues in risk adjusting rehabilitation outcomes in observational studies of routine clinical practice or for management, such as assessing quality or costs of care. Risk adjusting rehabilitation outcomes is more difficult than risk adjusting other clinical results, such as outcomes of many acute care services. At the outset, characterizing rehabilitation interventions is frequently difficult. Furthermore, outcomes are diverse and depend on myriad factors, including patients' physical and cognitive abilities, underlying medical diseases, sensory and emotional factors, willingness to participate in care, and supportive environments. No risk-adjustment approach can control for every factor affecting outcomes of care. Knowing which risk factors are missing helps guide interpretation of the results and determines how well risk-adjusted outcomes fairly compare providers or treatments.

Diagnosis-Related Groups↗
PubMed1995

The practice of risk adjustment.

This article focuses on risk adjustment under health care reform. It examines reasons for risk adjustment, how those reasons affect the method of adjustment, and practical issues that arise in the process of adjusting for risk. The paper concludes that adjusting payments to health plans makes the most sense. It advocates a risk adjustment agency that is regionally based and supportive of the public good. For risk adjustment to work, there must be a well-defined market and barriers to entry and exit.

Actuarial Analysis↗