Quality Measurement and
Risk Adjustment:
Empirical Analysis
Meeting #1. Proposed Methodology
HEALTH ECONOMICS PROGRAM
Overview

Context for statewide quality reporting and
incentive payment systems, and risk
adjustment

Health equity

MDH’s risk adjustment assessment

MN Community Measurement’s risk
adjustment activities

University of Minnesota’s empirical analysis
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Minnesota’s 2008
Health Reform Law





Establish standards for measuring
quality of health outcomes
Develop a standardized set of measures
Establish a system for risk adjusting
quality measures
Develop a system of quality incentive
payments (QIPS)
Health plans use the standardized
quality measures
3
Risk Adjustment:
What, Why, Where, and How?

What?


Why?


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Patient populations vary in characteristics (e.g., health status,
comorbidities, socio-demographic factors) that influence patient outcomes;
some of these characteristics are outside provider control
The goal of risk adjustment is to account for variability of patient
population characteristics outside of provider control to more fairly
compare providers’ quality of care
Where?



Statistical methods to account for patient-related characteristics … when
calculating performance measure scores
Public reporting
Pay for performance
How?

How to remove the effect of risk factors outside of provider control that
could affect a quality measure score
4
Current Risk Adjustment

Direct standardization
 Insurance product
(Medicare, Medicaid,
Commercial, Selfpay/uninsured)
 Patient severity
(depression)

Public process, research,
periodic revision
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Health Equity
Recommendation #7.
Strengthen the collection,
analysis, and use of data to
advance health equity

MDH must strengthen
coordination of data activities
related to health equity across all
divisions and programs, and
develop a long-term plan for
improving the collection, analysis,
reporting, dissemination and use
of health equity data
6
New Legislation

2014 Minnesota Laws, Chapter 312, Article 23, Section 10



Develop an implementation plan for stratifying measures based
on disability, race, ethnicity, language, and other sociodemographic factors
Assess the risk adjustment methodology to identify changes that
may be needed to alleviate potential harm and unintended
consequences of the existing methodology for patient populations
who experience health disparities and the providers who serve
them
2015 Minnesota Laws, Chapter 71, Article 9, Sections 4-7


Stratify five quality measures by race, ethnicity, preferred
language, and country of origin
Risk adjust quality measures using patient socio-demographic
factors
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Approach to Assessing
Risk Adjustment System
Objectives
•
•
Assess strengths and
weaknesses of current approach
Identify potential refinements:
• Methodologies
• Socio-demographic factors
• Data
Methodology
•
•
•
•
•
Identify short and long-term
options
Assess alignment of policy goals
with available tools
•
•
•
Empirically assess model
performance, including with
new data
Coordinate effort with work
that occurs in MNCM Risk
Adjustment & Segmentation
Subcommittee
Conduct literature review
Coordinate with DHS and MMB
Conduct key informant
interviews
Consider alignment of available
(refined) tools with policy goals
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Risk Adjustment and
Socio-demographic Factors
MDH
AHRQ
IOM &
Academia
National
Quality
Forum
MN
Community
Measurement
Local and
National
Activities
IMPACT
Act/HHS
MN Dept.
of
Human
Services
9
Complexities


How do we manage dual challenge?

Fairness to providers

Achieving best possible care for patients/transparency
Could adjusting for socio-demographic factors result in:

Masking disparities in outcomes?

Masking quality problems?

Creating different standards?

Reducing the incentive to improve and reduce disparities?

How do we balance measurement burden with policy goals?

Can we measure the right factors well?

Can we afford to wait for perfect information?
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Focus of Discussion:
U of M’s Empirical Analysis

Objectives

Data

Methodology

Assumptions

Limitations
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