Development of a Medicaid behavioral health case-mix model

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

INTRODUCTION

Effective risk adjustment is an aspect that is more and more given weight on the background of competitive health insurance systems and vital healthcare systems. The objective of this review was to obtain an overview of existing models of risk adjustment as well as on crucial weights in risk adjustment. Moreover, the predictive performance of selected methods in international healthcare systems should be analysed.

THEORY AND METHODS

A comprehensive, systematic literature review on methods of risk adjustment was conducted in terms of an encompassing, interdisciplinary examination of the related disciplines.

RESULTS

In general, several distinctions can be made: in terms of risk horizons, in terms of risk factors or in terms of the combination of indicators included. Within these, another differentiation by three levels seems reasonable: methods based on mortality risks, methods based on morbidity risks as well as those based on information on (self-reported) health status.

CONCLUSIONS AND DISCUSSION

After the final examination of different methods of risk adjustment it was shown that the methodology used to adjust risks varies. The models differ greatly in terms of their included morbidity indicators. The findings of this review can be used in the evaluation of integrated healthcare delivery systems and can be integrated into quality- and patient-oriented reimbursement of care providers in the design of healthcare contracts.

Is there one measure-of-fit that fits all? A taxonomy and review of measures-of-fit for risk-equalization models

This study provides a taxonomy of measures-of-fit that have been used for evaluating risk-equalization models since 2000 and discusses important properties of these measures, including variations in analytic method. It is important to consider the properties of measures-of-fit and variations in analytic method, because they influence the outcomes of evaluations that eventually serve as a basis for policymaking. Analysis of 81 eligible studies resulted in the identification of 71 unique measures that were divided into 3 categories based on treatment of the prediction error: measured based on squared errors, untransformed errors, and absolute errors. We conclude that no single measure-of-fit is best across situations. The choice of a measure depends on preferences about the treatment of the prediction error and the analytic method. If the objective is measuring financial incentives for risk selection, the only adequate evaluation method is to assess the predictive performance for non-random groups.

Predictors of mental health-related acute service utilisation and treatment costs in the 12 months following an acute psychiatric admission

OBJECTIVE

A key step in informing mental health resource allocation is to identify the predictors of service utilisation and costs. This project aims to identify the predictors of mental health-related acute service utilisation and treatment costs in the year following an acute public psychiatric hospital admission.

METHOD

A dataset containing administrative and routinely measured outcome data for 1 year before and after an acute psychiatric admission for 1757 public mental health patients was analysed. Multivariate regression models were developed to identify patient- and treatment-related predictors of four measures of service utilisation or cost: (a) duration of index admission; and, in the year after discharge from the index admission (b) acute psychiatric inpatient bed-days; (c) emergency department (ED) presentations; and (d) total acute mental health service costs. Split-sample cross-validation was used.

RESULTS

A diagnosis of psychosis, problems with living conditions and prior acute psychiatric inpatient bed-days predicted a longer duration of index admission, while prior ED presentations and self-harm predicted a shorter duration. A greater number of acute psychiatric inpatient bed-days in the year post-discharge were predicted by psychosis diagnosis, problems with living conditions and prior acute psychiatric inpatient admissions. The number of future ED presentations was predicted by past ED presentations. For total acute care costs, diagnosis of psychosis was the strongest predictor. Illness acuity and prior acute psychiatric inpatient admission also predicted higher costs, while self-harm predicted lower costs.

DISCUSSION

The development of effective models for predicting acute mental health treatment costs using existing administrative data is an essential step towards a workable activity-based funding model for mental health. Future studies would benefit from the inclusion of a wider range of variables, including ethnicity, clinical complexity, cognition, mental health legal status, electroconvulsive therapy, problems with activities of daily living and community contacts.

Comparing methods for identifying future high-cost mental health cases in Medicaid

OBJECTIVE

This article examines methods for identifying future high-cost cases of Medicaid-covered mental health care services.

METHODS

Florida Medicaid claims data are used to compare methods based on prior cost, and concurrent and prospective diagnosis-based models. Individuals with prior year expenditures in the top decile or with predicted expenditures in the top decile from the diagnosis-based models were expected to be high-cost individuals.

RESULTS

Individuals in the top decile of prior year costs averaged $13,684 (U.S. dollars) in costs in the following year with 50% remaining in the top decile of spending. Individuals classified as high cost by diagnosis-based models averaged $10,935 to $10,974, with 34% meeting the criteria for a high-cost case in the following year.

CONCLUSION

In contrast to research on high-costs cases for physical health care, prior cost was superior to diagnosis-based models at identifying future high cases for mental health care.