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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Related Experiment Video

Updated: Jul 10, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

The Medicaid Rx model: pharmacy-based risk adjustment for public programs.

T Gilmer¹, R Kronick, P Fishman

¹Department of Family and Preventive Medicine, University of California, San Diego 92093-0622, USA. tgilmer@ucsd.edu

|October 19, 2001

Summary

Pharmacy data can complement diagnostic data for better risk adjustment in Medicaid. Combining both data sources improves illness severity assessment, especially for TANF populations.

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Area of Science:

Health Services Research
Health Economics
Pharmacoeconomics

Background:

Traditional risk adjustment models rely on diagnostic data, but concerns exist regarding its availability and reliability.
Pharmacy data presents a potential alternative or complementary method for assessing illness severity and risk adjustment.

Purpose of the Study:

To develop and validate a pharmacy-based risk adjustment model for Supplemental Security Income (SSI) and Temporary Assistance for Needy Families (TANF) Medicaid populations.
To evaluate the performance of pharmacy data compared to diagnostic data in risk adjustment models.

Main Methods:

Developed the Medicaid Rx model, classifying National Drug Codes into risk-assessment categories.
Employed pharmacological review and empirical evaluation using data from 1990-1999.
Compared pharmacy and diagnostic classification for three chronic diseases and evaluated model performance using R2 statistics and simulated health plans.

Main Results:

Pharmacy and diagnostic classifications showed varying abilities in identifying specific chronic diseases.
Diagnostic models better predicted expenditures for disabled Medicaid beneficiaries, while pharmacy and diagnostic models performed similarly for TANF beneficiaries.
Models integrating both diagnostic and pharmacy data demonstrated superior overall performance.

Conclusions:

Combined pharmacy and diagnostic data yield superior risk adjustment model performance compared to using either source alone, particularly for TANF beneficiaries.
Further research is warranted to address concerns about prescribing pattern variations and potential incentives linked to pharmacy use in payment models.