Saltar al contenido principal

ETHNOS_APP

Inicio • Búsqueda • Revistas • Lista 0

The Medicaid Rx Model

Pharmacy-Based Risk Adjustment for Public Programs

Datos Bibliográficos

ID9099987
AutoresTodd Gilmer (0000-0002-8360-3522, University of California San Diego, autor de correspondencia), Richard Kronick (0000-0002-6219-0861, University of California San Diego, autor de correspondencia), Paul Fishman (0000-0003-3419-4539, University of Puget Sound), Theodore G Ganiats (0000-0002-3920-3708, University of California San Diego, autor de correspondencia)
Año2001
Volumen39
Número11
Páginas1188-1202
Fecha de publicación2001-11-01
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaMedical Care (JOURNAL)
Identificadores de la revistaISSN: 0025-7079 • E-ISSN: 1537-1948
EditorialOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/00005650-200111000-00006
PMID11606873
OpenAlexW2079202550
IdiomaEN
Citas recibidas25
Referencias citadas6

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

Actuarial science · Business · Family medicine · Health care · Machine learning · Medicaid · Medical diagnosis · MEDLINE · Payment · Predictive modelling · Test (biology) · Chronic Disease Management Strategies · Computer Science · Medication Adherence and Compliance · Medicine · Pharmaceutical Practices and Patient Outcomes · Pharmacy

  • Assessing Risk-Adjustment Approaches under Non-Random Selection

    Open Access•Harold S Luft, R Adams Dudley•INQUIRY The Journal of Health…•2004

  • Associations between Adverse Childhood Experiences and Emergency Department Utilization in an Adult Medicaid Population

    Open Access•Kristin L Scott, Hannah Cohen-Cline•International Journal of…•2022

  • Home and Community-Based Waivers for Disabled Adults

    Open Access•Courtney H Van Houtven, Marisa Elena Domino•INQUIRY The Journal of Health…•2005

  • Evaluation of the Medicaid Buy-In Program in Washington State

    Open Access•Melissa Ford Shah, David Mancuso et al.•Journal of Disability Policy…•2012

  • The influence of socio-economic status and multimorbidity patterns on healthcare costs

    Open Access•Raymond N Kuo, Mei-Shu Lai•International Journal for Equity…•2013

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

    Open Access•Christin Juhnke, Susanne Bethge et al.•International Journal of…•2016

  • A medication-estimated health status measure for predicting primary care visits

    Open Access•Abdullah Ahmed Dhabali, Rahmat Awang•Health Policy and Planning•2010

  • Using risk adjustment approaches in child welfare performance measurement

    Open Access•Ramesh Raghavan•Children and Youth Services Review•2010

  • Predicting Homelessness among Emerging Adults Aging Out of Foster Care

    Open Access•Melissa Ford Shah, Qinghua Liu et al.•American Journal of Community…•2017

  • Psychotropic Drug Use Among Preschool Children in the Medicaid Program From 36 States

    Lauren D Garfield, Derek S Brown et al.•American Journal of Public Health•2015

  • Health Care Use and Spending for Medicaid Enrollees in Federally Qualified Health Centers Versus Other Primary Care Settings

    Robert S Nocon, Sang Mee Lee et al.•American Journal of Public Health•2016

  • Quantifying the Physician Contribution to Managed Care Pharmacy Expenses

    Mark E Cowen, Robert L Strawderman•Medical Care•2002

  • Drivers of High-cost Medical Complexity in a Medicaid Population

    Open Access•David Labby, Bill Wright et al.•Medical Care•2020

  • Updating the Chronic Illness and Disability Payment System

    Open Access•Todd Gilmer, Richard Kronick•Medical Care•2024

  • Latent Class Analysis to Represent Social Determinant of Health Risk Groups in the Medicaid Cohort of the District of Columbia

    Melissa L McCarthy, Zhaonian Zheng et al.•Medical Care•2021

  • Predictive Modeling of Total Healthcare Costs Using Pharmacy Claims Data

    Christopher A Powers, Christina Meyer et al.•Medical Care•2005

  • Predicting Resource Utilization in a Veterans Health Administration Primary Care Population

    Terry L Wahls, Mitchell J Barnett et al.•Medical Care•2004

  • Pharmacy- and Diagnosis-Based Risk Adjustment for Children With Medicaid

    Karen Kuhlthau, Timothy G Ferris et al.•Medical Care•2005

  • Predicting Healthcare Utilization Using a Pharmacy-based Metric With the WHO’s Anatomic Therapeutic Chemical Algorithm

    Raymond N Kuo, Yaa-Hui Dong et al.•Medical Care•2011

  • Improving Risk Equalization Using Multiple-year High Cost as a Health Indicator

    Richard C van Kleef, René C J A Van Vliet•Medical Care•2012

  • Evaluation of the Washington State Screening, Brief Intervention, and Referral to Treatment Project

    Sharon Estee, Thomas M Wickizer et al.•Medical Care•2010

  • Risk Adjustment Using Automated Ambulatory Pharmacy Data

    Paul Fishman, Paul A Fishman et al.•Medical Care•2003

  • Incorporating Prescription Drugs Into Affordable Care Act Risk Adjustment

    Gregory C Pope, Andrew Pearlman et al.•Medical Care•2020

  • The Impact of Integrated Case Management on Health Services Use and Spending Among Nonelderly Adult Medicaid Enrollees

    Lindsay M Sabik, Gloria J Bazzoli et al.•Medical Care•2016

  • The Best of Both Worlds

    R Adams Dudley, Carol A Medlin et al.•Medical Care•2003

  • Development and Estimation of a Pediatric Chronic Disease Score Using Automated Pharmacy Data

    Paul Fishman, Paul A Fishman et al.•Medical Care•1999

  • A Chronic Disease Score with Empirically Derived Weights

    Daniel O Clark, MICHAEL VON KORFF et al.•Medical Care•1995

Obras citantes distintas25
Citas por año1,04
Intervalo de citas2002 - 2024 (23)
Velocidad de citaciónrecent
Altamente citadoNo
Tipos de citaNeutras: 25
Ethnos_APP • Proyecto Open Source • Licencia MIT • Frontend v2.0.0 • Privacidad y Cookies • Documentación de la API: api.ethnos.app/docs • Código de la API: GitHub • DOI: 10.5281/zenodo.17049435 • Código del Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae