Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Risk Adjustment”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources
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↗
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↗
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↗
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↗
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↗
PubMed1999

Risk-adjusted in-hospital death rates for peer hospitals in rural and urban regions.

The purpose of this research project was to compare inpatient mortality rates for rural hospitals with mortality rates of urban hospitals of given sizes and ranges of service. Statistical adjustments for risk were made in the probability of death during hospitalization for 43,000 patients across 166 hospitals by age, gender, principal diagnosis, principal surgical procedure, characteristics of the secondary diagnoses, and whether or not cancer was a secondary diagnosis. Eighty-three small hospitals that had a relatively unspecialized range of services constituted the study group. Patient characteristics of this study group were moderately representative of the national population. A standardized score was calculated for each hospital using a formula based on the actual hospital death rate and the death rate expected for a given hospital with patients of the same demographic and medical characteristics. Patients admitted to hospitals in nonmetropolitan areas had a mortality rate of 0.41 percent compared with a mortality rate of 0.66 percent in peer hospitals in metropolitan areas. After mortality rates were risk-adjusted and converted to z scores, nonmetropolitan areas had an average z of +0.16, and metropolitan areas had an average z of -0.25, where positive z scores reflect a lower-than-average adjusted mortality rate. The metropolitan-nonmetropolitan (urban-rural) difference was not statistically significant, but it is meaningful in that rural hospitals tended to have a lower adjusted mortality rate than urban hospitals of the same size and type, indicating that rural hospitals had the same or lower adjusted mortality rates. The possibility of urban hospitals having riskier patients was minimized but could not be definitively ruled out. Taken together with other studies, the data are consistent with the view that small rural hospitals generally make appropriate transfer decisions for severely ill patients and provide quality care for retained patients.

Adolescent↗
PubMed2000

Risk adjusted and population based studies of the outcome for high risk infants in Scotland and Australia. International Neonatal Network, Scottish Neonatal Consultants, Nurses Collaborative Study Group.

OBJECTIVES: To compare outcomes of care in selected neonatal intensive care units (NICUs) for very low birthweight (VLBW) or preterm infants in Scotland and Australia (study 1) and perinatal care for all VLBW infants in both countries (study 2). DESIGN: Study 1: risk adjusted cohort study; study 2: population based cohort study. SUBJECTS: Study 1: all 2621 infants of < 1500 g birth weight or < 31 weeks' gestation admitted to a volunteer sample of hospitals comprising eight of all 17 Scottish NICUs and six of all 12 tertiary NICUs in New South Wales and Queensland in 1993-1994; study 2: all 5986 infants of 500-1499 g birth weight registered as live born in Scotland and Australia in 1993-1994. MAIN OUTCOMES: Study 1: (a) hospital death; (b) death or cerebral damage, each adjusted for gestation and CRIB (clinical risk index for babies); study 2: neonatal (28 day) mortality. RESULTS: Study 1. Data were obtained for 1628 admissions in six Australian NICUs, 775 in five Scottish tertiary NICUs, and 148 in three Scottish non-tertiary NICUs. Crude hospital death rates were 13%, 22%, and 22% respectively. Risk adjusted hospital mortality was about 50% higher in Scottish than in Australian NICUs (adjusted mortality ratio 1.46, 95% confidence interval (CI) 1.29 to 1.63, p < 0.001). There was no difference in risk adjusted outcomes between Scottish tertiary and non-tertiary NICUs. After risk adjustment, death or cerebral damage was more common in Scottish than Australian NICUs (odds ratio 1.9, 95% CI 1.5 to 2.5). Both these risk adjusted adverse outcomes remained more common in Scottish than Australian NICUs after excluding all infants < 28 weeks' gestation from the comparison. Study 2. Population based neonatal mortality in infants of 500-1499 g was higher in Scotland (20.3%) than Australia (16.6%) (relative risk 1.22, 95% CI 1.08 to 1. 39, p = 0.002). In a post hoc analysis, neonatal mortality was also higher in England and Wales than in Australia. CONCLUSIONS: Study 1: outcome was better in the Australian NICUs. Study 2: perinatal outcome was better in Australia. Both results may be consistent, at least in part, with differences in the organisation and implementation of neonatal care.

Australia↗
PubMed2000

Performance status of health care facilities changes with risk adjustment of HbA1c.

OBJECTIVE: To develop a risk adjustment method for HbA1c, based solely on administrative data and to determine the extent to which risk-adjusted HbA1c changes the identification of high- or low-performing medical facilities. RESEARCH DESIGN AND METHODS: Through use of pharmacy records, 204,472 diabetic patients were identified for federal fiscal year 1996 (FY96). Complete information (HbA1c levels, demographic data, inpatient records, outpatient pharmacy utilization records) was available on 38,173 predominantly male patients from 48 Veterans Health Administration (VHA) medical facilities. Hierarchical mixed-effects models were used to estimate risk-adjusted unique facility-level HbA1c. RESULTS: Predicted HbA1c demonstrated expected patterns for major factors known to influence glycemic control. Poorer glycemic control was seen in minorities and patients with greater disease severity, longer duration of disease (using treatment type or presence of amputation as surrogates), and more extensive comorbidity (measured by an adapted Charlson index). Better glycemic control was seen in Caucasians, older diabetic patients, and patients with higher outpatient utilization. The number of performance outliers was reduced as a result of risk adjustment. For mean HbA1c levels, 7 facilities that were initially identified as statistically significant outliers were no longer outliers after risk adjustment. For high-risk HbA1c (>9.5%) rates, 12 facilities that were initially identified as statistically significant outliers were no longer outliers after risk adjustment. CONCLUSIONS: Risk adjustment using only administrative data resulted in substantial changes in identification of high or low performers compared with non-risk-adjusted HbA1c. Although our findings are exploratory, risk adjustment using administrative data may be a necessary and achievable step in quality assessment of diabetes care measured by rates of high-risk HbA1c (>9.5%).

Adult↗
PubMed1998

Period of adjustment. Health plans experiment with new clinical risk adjustment strategies.

Health plans are moving forward with the concept of clinical risk adjustment--essentially, paying physicians more for taking on sicker patients--in a variety of forms. The challenges, from the policy and strategy levels to the data collection and analysis level, are many. Yet, despite some concerns on the part of provider execs, observers say clinical risk adjustment's time has come. Meanwhile, the issues remain many for both plans and providers.

Ambulatory Care↗
PubMed1997

Comparing risk-adjustment methods for provider profiling.

Risk-adjustment and provider profiling have become common terms as the medical profession attempts to measure quality and assess value in health care. One of the areas of care most thoroughly developed in this regard is quality assessment for coronary artery bypass grafting (CABG). Because in-hospital mortality following CABG has been studied extensively, risk-adjustment mechanisms are already being used in this area for provider profiling. This study compares eight different risk-adjustment methods as applied to a CABG surgery population of 28 providers. Five of the methods use an external risk-adjustment algorithm developed in an independent population, while the other three rely on an internally developed logistic model. The purposes of this study are to: (i) create a common metric by which to display the results of these various risk-adjustment methodologies with regard to dichotomous outcomes such as in-hospital mortality, and (ii) to compare how these risk-adjustment methods quantify the 'outlier' standing of providers. Section 2 describes the data, the external and internal risk-adjustment algorithms, and eight approaches to provider profiling. Section 3 then demonstrates the results of applying these methods on a data set specifically collected for quality improvement.

Aged↗
PubMed1998

The current state of risk adjustment technology for capitation.

Risk adjustment for the purposes of making capitated payments better reflect the expected costs of medical care is a technology that is now being applied in the public and private sector. This article reviews the characteristics of many of the risk adjuster methods that have been put forward in recent years. Included are models based on diagnoses from inpatient hospital data, and from inpatient and ambulatory data. Models for the general population, Medicaid, and Medicare are discussed. Caveats in comparing models are also presented.

Adult↗
PubMed1997

Risk-adjusted capitation payments for catastrophic risks based on multi-year prior costs.

In many countries regulated competition among health insurance companies has recently been proposed or implemented. A crucial issue is whether or not the benefits package offered by competing insurers should also cover catastrophic risks (like several forms of expensive long-term care) in addition to non-catastrophic risks (like hospital care and physician services). In 1988 the Dutch government proposed compulsory national health insurance based on regulated competition among insurer as well as among providers of care. The competing insurers should offer a benefits package covering both non-catastrophic risks and catastrophic risks. The insurers would be largely financed via risk-adjusted capitation payments. The government intended to use a capitation formula that is, besides some demographic variables, based on multi-year prior costs. This paper presents the results of an explorative empirical analysis of the possible consequences of such a capitation formula for catastrophic risks. The main conclusion is that this formula would be inadequate because it would leave ample room for cream skimming.

Capitation Fee↗
PubMed1998

Risk adjustment and the trade-off between efficiency and risk selection: an application of the theory of fair compensation.

We exploit the similarity between the problem of risk adjustment with prospective reimbursement schemes in the health care sector and the problem of fair compensation analysed in the social choice literature. The starting point is the distinction between two sets of variables in the explanation of medical expenditures: those for which the insurers (or the providers) can be held responsible, and those for which they have to be compensated. Using this partitioning the objectives of cost-efficiency and no risk selection can be expressed in terms of two simple axioms. If the medical expenditure function is additively separable in the two sets of variables, there exists a natural division rule which is analogous to the standard linear risk adjustment schemes. We show how this rule should be applied if the total level of actual medical expenditures is different from the budget to be divided over the insurers (or providers) and how information from the disturbances in the regression equation can be used in an optimal way. We discuss the analogy with mixed reimbursement systems. If the medical expenditure function is not additively separable in the two sets of variables, the conflict between efficiency and risk selection is unavoidable, even if one has perfect information about that function. The theoretical results are illustrated with empirical results derived from the Belgian setting where the move towards prospective reimbursement of the mutualities has necessitated the introduction of a risk adjustment formula.

Belgium↗
PubMed2001

Risk-adjusted surgical outcomes.

Measures of risk-adjusted outcome are particularly suited for the assessment of the quality of surgical care. The reliability of measures of quality that use surgical outcomes is enhanced by prospective data acquisition and should be adjusted for the preoperative severity of illness. Such measures should be based only on reliable and validated data, and they should apply state-of-the-art analytical methods. The risk-adjusted postoperative mortality rate is useful as a quality measure only in specialties and operations expected to have a high rate of postoperative deaths. Risk-adjusted complications are more common but are limited as a comparative measure of quality by a lack of uniform definitions and data collection mechanisms. In specialties in which the expected postoperative mortality is low, risk-adjusted functional outcomes are promising measures for the assessment of the quality of surgical care. Measures of cost and patient satisfaction should also be incorporated in systems designed to measure the quality and cost-effectiveness of surgical care.

Activities of Daily Living↗
PubMed1994

Patients at risk: health reform and risk adjustment.

The Clinton proposal recognizes the need for successful risk adjustment and calls for the National Health Board to promulgate a risk adjustment formula by 1 April 1995. Unfortunately, risk adjustment technology is primitive; using observable characteristics such as age only slightly ameliorates the flawed incentives of not adjusting at all. Without major improvements in risk adjustment technology we face a trade-off between giving plans an incentive to select good risks and an incentive to produce at lowest cost. Pure capitation maximizes both incentives; pure fee-for-service minimizes both. I suggest experimentation with paying plans partly on the basis of risk-adjusted capitation and partly on the basis of a fee schedule reflecting actual use (partial capitation). In the draft Clinton plan, the option given to alliances not to offer plans priced above 120 percent of the weighted average premium appears to assume better risk adjustment ability than is now possible. This option should be relaxed or abandoned.

Actuarial Analysis↗
PubMed1997

Validating risk-adjusted surgical outcomes: site visit assessment of process and structure. National VA Surgical Risk Study.

BACKGROUND: Risk-adjusted mortality and morbidity rates are often used as measures of the quality of surgical care. This study was conducted to determine the validity of risk-adjusted surgical morbidity and mortality rates as measures of quality of care by assessing the process and structure of care in surgical services with higher-than-expected and lower-than-expected risk-adjusted 30-day mortality and morbidity rates. STUDY DESIGN: A structural survey of 44 Veterans Affairs Medical Center surgical services and site visits to 20 surgical services with higher-than-expected and lower-than-expected risk-adjusted outcomes were conducted. Main outcome measures included assessment of technology and equipment, technical competence of staff, leadership, relationship with other services, monitoring of quality of care, coordination of work, relationship with affiliated institutions, and overall quality of care. RESULTS: Surgical services with lower-than-expected risk-adjusted surgical morbidity and mortality rates had significantly more equipment available in surgical intensive care units than did services with higher-than-expected outcomes (4.3 versus 2.9, p < 0.05). Site-visitor ratings of overall quality of care were significantly higher for surgical services with lower-than-expected morbidity and mortality rates (6.1 versus 4.5 for high outliers, p < 0.05); technology and equipment were rated significantly better among low-outlier services (7.1 versus 4.8 for high outliers, p < 0.001). Masked site-visit teams correctly predicted the outlier status (high versus low) of 17 of the 20 surgical services visited (p < 0.001). CONCLUSIONS: Significant differences in several dimensions of process and structure of the delivery of surgical care are associated with differences in risk-adjusted surgical morbidity and mortality rates among 44 Veterans Affairs Medical Centers.

Hospital Mortality↗