# Presentation: 311718

## Outpatient Encounter Data for Risk Adjustment:  Strategic Issues for Medicare & Medicaid

- By W. Pete Welch, Ph., D. and
- David Knutson
- NAHDO, Dec. 5, 2000

## Introduction

- Medicare
- Jan. 2000: implemented risk adjustment based on inpatient data.
- Fall 2000: starting to collect outpatient data for risk adjustment in 2004.
- Medicaid in CO, MD, & MN
- Other state-level programs: MN & WA

## Criteria: Risk Adjustment

- Predictive accuracy
- Incentives
- Data quality
- Data availability & reporting/auditing burden
- HCFA & scholars have focused on first 2 criteria; we’ll focus on second 2.

## Context

- Fee-for-service (FFS) claims convenient for HCFA & scholars.
- Made important progress, but claims are mile wide & inch deep.
- Minimal written evaluation (by HCFA, plans, & scholars) of strategic data issues.
- Today we are only critiquing the _new status quo_, not offering alternatives.

## Preview: Take-Home Messages

- FFS diagnosis (dx) data should _not_ be treated as the _gold standard_.
- Even well-run plans may be seriously _underpaid_--if risk adjustment is based on encounter data.
- Truly _comparable diagnosis data_ from the FFS and HMO sectors is a will-o’-the-wisp.
- Ideally, plans would have a single _information system_ to meet internal & external demands.

## FFS Diagnosis Data

**FFS diagnosis data should not be treated as the gold standard.**
- Minimal penalties for careless coding in physician’s office.
- Dx coding = clinical judgments of varying uncertainty at one time, e.g., meningitis.
- Closer you look, the more dxs you find.

## Consistency Over Time

- Anomalous nonpersistence of chronic conditions in claims
- MedPAC (3/98): Of beneficiaries w/ a dx in FFS claims in 1994, what % had dx following year?:
- CHF, 61%
- COPD, 62%
- quadriplegia/paraplegia, 59%

## Consistency w/ Medical Records

- Fowles, et al. (1995) compared FFS claims & MD office records.
- Complete coding could increase measured prevalence by about 1/2.
- About 1/3 of claims lacked documentation in medical record.

## Consistency Over Space #1

- Dartmouth Atlas methodology
- 306 hospital referral regions
- echo cardiograms per 1,000 FFS beneficiaries
- Results: 90th vs 10th percentile regions
- 171 vs. 79 echo cardiograms per 1,000
- a ratio of greater than 2 to 1
- Beneficiary’s likelihood of being diagnosed depends on where one lives.

## Consistency Over Space #2

## Potential Underpayment #1

**Even well-run plans may be seriously underpaid if risk adjustment is based on outpatient encounter data.**
- Suppose average payment = $100 PMPM.
- Risk adjustment systems pay less than $40 PMPM for enrollees with no dx.

## Potential Underpayment #2

- Consider a plan with complete data on 90% of its enrollees, no data on the rest, and no selection bias.
- It would be
- paid $100 PMPM for 90% of enrollees w/ data.
- paid $40 PMPM for 10% of enrollees w/o data.
- underpaid by 6%.
- Margins often less than 2% (Best, 1999).

## Federal Audits--Prospects

- Federal gov’t does not systematically audit dx codes on FFS outpatient claims.
- A dx code for CHF, for instance, increases payment by $12,000 under PIP-DCGs.
- So HCFA, GAO, & IG will presumably be auditing encounter data.

## Federal Audits--Issues

- HCFA will need to specify standards of evidence:
- Hypertension ($4,200 add-on payment)
- How measured if on medication?
- Closer you look, the more dxs you find.
- Documentation
- Fowles finding
- Dx under uncertainty

- Intentionally left blank for the handout.

## Comparable Diagnosis Data #1

- HMO data may understate prevalence of chronic diseases (more than FFS):
- Encounter data often _not_ used for payment.
- Federal audits of dx much more of a concern to HMOs than FFS providers.

## Comparable Diagnosis Data #2

- But HMO data may have potential to more accurately reflect prevalence of selected dx because of disease management.
- Recent AAHP survey of DM programs
- Diabetes, asthma, & CHF--75+% of plans
- Other conditions--much lower %
- Note, to identify enrollees w/ a disease, HMOs use several types of data.

## Comparable Diagnosis Data #3

**Truly comparable diagnosis data from the FFS and HMO sectors is a will-o’-the-wisp**.
- Implications for
- policy options
- implementation (e.g., edits)

## Encounter Data-Based Future

- Encounter-data approach requires HMOs to mimic FFS.
- If HMOs increasingly identify enrollees who truly have a chronic disease, this is likely to be erroneously interpreted as upcoding.
- Increased audits

## Alternative Future #1

**Ideally, plans would have a single information system to meet both internal and external demands.**
- Continue to submit inpatient encounter data (Medicare).

## Alternative Future #2

- Perhaps give plans flexibility on types of data they submit (e.g., pharmacy).
- Perhaps start w/ a limited number of dxs.
- Define standards for audits concurrent with dx-based payment system.
- In general, focus on details, where the devil hangs out.