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van Kleef RC, Reuser M, Stam PJA, van de Ven WPMM. A framework for ex-ante evaluation of the potential effects of risk equalization and risk sharing in health insurance markets with regulated competition. HEALTH ECONOMICS REVIEW 2024; 14:57. [PMID: 39046547 PMCID: PMC11267970 DOI: 10.1186/s13561-024-00540-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 07/16/2024] [Indexed: 07/25/2024]
Abstract
Many health insurance markets are organized by principles of regulated competition. Regulators of these markets typically apply risk equalization (aka risk adjustment) and risk sharing to mitigate risk selection. Risk equalization and risk sharing can have various positive and negative effects on efficiency and fairness. This paper provides a comprehensive framework for ex-ante evaluation of these effects. In a first step, we distinguish 22 potential effects. In a second step, we summarize and discuss quantitative measures used for evaluating risk equalization and risk sharing schemes in academic research. To underline the relevance of our work, we compare our framework with an existing framework that was previously used in the Dutch regulated health insurance market. We conclude that this framework is incomplete and uses inappropriate measures. To avoid suboptimal policy choices, we recommend policymakers (1) to consider the entire spectrum of potential effects and (2) to select their measures carefully.
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Affiliation(s)
- Richard C van Kleef
- Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Erasmus Centre for Health Economics Rotterdam (EsCHER), Burgemeester Oudlaan 50, Rotterdam, 3062 PA, The Netherlands.
| | - Mieke Reuser
- National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, Bilthoven, 3721 MA, The Netherlands
| | - Pieter J A Stam
- School of Business and Economics, Ethics, Governance and Society, Vrije Universiteit Amsterdam, De Boelelaan 1085, Amsterdam, 1081 HV, The Netherlands
| | - Wynand P M M van de Ven
- Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Erasmus Centre for Health Economics Rotterdam (EsCHER), Burgemeester Oudlaan 50, Rotterdam, 3062 PA, The Netherlands
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van Kleef RC, Reuser M, McGuire TG, Armstrong J, Beck K, Brammli-Greenberg S, Ellis RP, Paolucci F, Schokkaert E, Wasem J. Scope and Incentives for Risk Selection in Health Insurance Markets With Regulated Competition: A Conceptual Framework and International Comparison. Med Care Res Rev 2024; 81:175-194. [PMID: 38284550 PMCID: PMC11092299 DOI: 10.1177/10775587231222584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 12/03/2023] [Indexed: 01/30/2024]
Abstract
In health insurance markets with regulated competition, regulators face the challenge of preventing risk selection. This paper provides a framework for analyzing the scope (i.e., potential actions by insurers and consumers) and incentives for risk selection in such markets. Our approach consists of three steps. First, we describe four types of risk selection: (a) selection by consumers in and out of the market, (b) selection by consumers between high- and low-value plans, (c) selection by insurers via plan design, and (d) selection by insurers via other channels such as marketing, customer service, and supplementary insurance. In a second step, we develop a conceptual framework of how regulation and features of health insurance markets affect the scope and incentives for risk selection along these four dimensions. In a third step, we use this framework to compare nine health insurance markets with regulated competition in Australia, Europe, Israel, and the United States.
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Affiliation(s)
- Richard C. van Kleef
- Erasmus Centre for Health Economics Rotterdam (EsCHER), Erasmus University Rotterdam, The Netherlands
| | - Mieke Reuser
- National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | | | - John Armstrong
- Erasmus Centre for Health Economics Rotterdam (EsCHER), Erasmus University Rotterdam, The Netherlands
| | | | | | | | | | | | - Juergen Wasem
- University of Duisburg-Essen, Nordrhein-Westfalen, Germany
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Schaekermann M, Spitz T, Pyles M, Cole-Lewis H, Wulczyn E, Pfohl SR, Martin D, Jaroensri R, Keeling G, Liu Y, Farquhar S, Xue Q, Lester J, Hughes C, Strachan P, Tan F, Bui P, Mermel CH, Peng LH, Matias Y, Corrado GS, Webster DR, Virmani S, Semturs C, Liu Y, Horn I, Cameron Chen PH. Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study. EClinicalMedicine 2024; 70:102479. [PMID: 38685924 PMCID: PMC11056401 DOI: 10.1016/j.eclinm.2024.102479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 01/16/2024] [Accepted: 01/25/2024] [Indexed: 05/02/2024] Open
Abstract
Background Artificial intelligence (AI) has repeatedly been shown to encode historical inequities in healthcare. We aimed to develop a framework to quantitatively assess the performance equity of health AI technologies and to illustrate its utility via a case study. Methods Here, we propose a methodology to assess whether health AI technologies prioritise performance for patient populations experiencing worse outcomes, that is complementary to existing fairness metrics. We developed the Health Equity Assessment of machine Learning performance (HEAL) framework designed to quantitatively assess the performance equity of health AI technologies via a four-step interdisciplinary process to understand and quantify domain-specific criteria, and the resulting HEAL metric. As an illustrative case study (analysis conducted between October 2022 and January 2023), we applied the HEAL framework to a dermatology AI model. A set of 5420 teledermatology cases (store-and-forward cases from patients of 20 years or older, submitted from primary care providers in the USA and skin cancer clinics in Australia), enriched for diversity in age, sex and race/ethnicity, was used to retrospectively evaluate the AI model's HEAL metric, defined as the likelihood that the AI model performs better for subpopulations with worse average health outcomes as compared to others. The likelihood that AI performance was anticorrelated to pre-existing health outcomes was estimated using bootstrap methods as the probability that the negated Spearman's rank correlation coefficient (i.e., "R") was greater than zero. Positive values of R suggest that subpopulations with poorer health outcomes have better AI model performance. Thus, the HEAL metric, defined as p (R >0), measures how likely the AI technology is to prioritise performance for subpopulations with worse average health outcomes as compared to others (presented as a percentage below). Health outcomes were quantified as disability-adjusted life years (DALYs) when grouping by sex and age, and years of life lost (YLLs) when grouping by race/ethnicity. AI performance was measured as top-3 agreement with the reference diagnosis from a panel of 3 dermatologists per case. Findings Across all dermatologic conditions, the HEAL metric was 80.5% for prioritizing AI performance of racial/ethnic subpopulations based on YLLs, and 92.1% and 0.0% respectively for prioritizing AI performance of sex and age subpopulations based on DALYs. Certain dermatologic conditions were significantly associated with greater AI model performance compared to a reference category of less common conditions. For skin cancer conditions, the HEAL metric was 73.8% for prioritizing AI performance of age subpopulations based on DALYs. Interpretation Analysis using the proposed HEAL framework showed that the dermatology AI model prioritised performance for race/ethnicity, sex (all conditions) and age (cancer conditions) subpopulations with respect to pre-existing health disparities. More work is needed to investigate ways of promoting equitable AI performance across age for non-cancer conditions and to better understand how AI models can contribute towards improving equity in health outcomes. Funding Google LLC.
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Affiliation(s)
| | | | - Malcolm Pyles
- Advanced Clinical, Deerfield, IL, USA
- Department of Dermatology, Cleveland Clinic, Cleveland, OH, USA
| | | | | | | | | | | | | | - Yuan Liu
- Google Health, Mountain View, CA, USA
| | | | | | - Jenna Lester
- Advanced Clinical, Deerfield, IL, USA
- Department of Dermatology, University of California, San Francisco, CA, USA
| | | | | | | | - Peggy Bui
- Google Health, Mountain View, CA, USA
| | | | | | | | | | | | | | | | - Yun Liu
- Google Health, Mountain View, CA, USA
| | - Ivor Horn
- Google Health, Mountain View, CA, USA
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Parbhoo S, Wawira Gichoya J, Celi LA, de la Hoz MÁA. Operationalising fairness in medical algorithms. BMJ Health Care Inform 2022; 29:bmjhci-2022-100617. [PMID: 35688512 PMCID: PMC9189822 DOI: 10.1136/bmjhci-2022-100617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 05/24/2022] [Indexed: 11/25/2022] Open
Affiliation(s)
- Sonali Parbhoo
- Harvard Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Judy Wawira Gichoya
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Harvard-MIT Division of Health Sciences and Technology, Cambridge, Massachusetts, USA
- Division of Pulmonary Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
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