Literature DB >> 31928858

A systematic review of case-mix models for home health care payment: Making sense of variation.

Anne O E van den Bulck1, Maud H de Korte2, Arianne M J Elissen3, Silke F Metzelthin4, Misja C Mikkers5, Dirk Ruwaard6.   

Abstract

BACKGROUND: Case-mix based payment of health care services offers potential to contain expenditure growth and simultaneously support needs-based care provision. However, limited evidence exists on its application in home health care (HHC). Therefore, this study aimed to synthesize available international literature on existing case-mix models for HHC payment.
METHODS: We performed a systematic review of scientific literature, supplemented with grey literature. We searched for literature using six scientific databases, reference lists, expert consultation, and targeted websites. Data on study design, case-mix model attributes, and conclusions were extracted narratively.
RESULTS: Of 3303 references found, 22 scientific studies and 27 grey documents met eligibility criteria. Eight case-mix models for HHC were identified, from the US, Canada, New Zealand, Australia, and Germany. Three countries have implemented a case-mix model as part of a HHC payment system. Different combinations of in total 127 unique case-mix predictors are included across models to predict HHC use. Case-mix models also differ in targeted services, operationalization, and outcome measures and predictive power.
CONCLUSIONS: Case-mix based payment is not yet widely used within HHC. Multiple varieties were found between HHC case-mix models, and no one best form of a model seems to exist. Even though varieties are partly inevitable due to country-specific contexts, developing a shared vision in case-mix model attributes would be key to achieving efficient, needs-based HHC.
Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.

Keywords:  Casemix; Classification; Home care services; Prospective payment system; Systematic review

Mesh:

Year:  2020        PMID: 31928858     DOI: 10.1016/j.healthpol.2019.12.012

Source DB:  PubMed          Journal:  Health Policy        ISSN: 0168-8510            Impact factor:   2.980


  1 in total

1.  Identifying client characteristics to predict homecare use more accurately: a Delphi-study involving nurses and homecare purchasing specialists.

Authors:  Anne O E van den Bulck; Arianne M J Elissen; Silke F Metzelthin; Maud H de Korte; Gertjan S Verhoeven; Teuntje A T de Witte-Breure; Lieuwe C van der Weij; Misja C Mikkers; Dirk Ruwaard
Journal:  BMC Health Serv Res       Date:  2022-03-25       Impact factor: 2.655

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.