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

Abstract

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.

Cardiovascular Risk in Workers of the Area of Prevention and Emergency Control of the Oil Sector

“…It constitutes a basic pillar and in different ways it is widely used for therapeutic decision making based on the risk estimation provided by the method when introducing the risk characteristics of the specific patient (Juhnke et al, 2016).…”

Tailoring integrated care services for high-risk patients with multiple chronic conditions: a risk stratification approach using cluster analysis

“…In general terms, candidates for case management belong to the top 5% risk stratum of the population and are identified using a variety of ready-to-use risk stratification tools, including clinical risk groups (CRGs), adjusted clinical groups, diagnosis-related groups, diagnostic cost groups or the senior segmentation algorithm among others [ 9 , 11 – 13 ]. A set of common services can be provided where risk stratification produces a homogeneous group of patients, and if needs are appropriately addressed, case management will fit patients in a cost-effective way, avoiding wasteful, unnecessary care.…”

Identifying patients using antidepressants for the treatment of depression: A predictive algorithm for use in pharmaceutical and medical claims data

“…As pharmaceutical dispensing data are a reliable source of health information, often available for whole populations, they are frequently used to identify individuals with chronic diseases including depression. Traditionally, this has been in the context of adjusting for comorbidities in epidemiologic studies and health system resource allocation (for example, Lamers and Van Vliet and Juhnke et al), using indices such as RxRisk that map pharmaceuticals to disease categories.…”

“…A predictive algorithm meeting this criterion has previously been developed 18 ; however, it identified depression only among new starters of antidepressants. All of the abovementioned applications of pharmaceutical-based measures of depression, 4,5,[7][8][9][10][11][12][13] however, are based on both existing and new users of antidepressants. It is possible that prevalent users of antidepressants differ from new users on potentially predictive factors such as type and dose of antidepressant used.…”