Jim Verdier, Mathematica Policy Research€¦ · Shorter lead time to start using data Most recent data are for CY 2009; CY 2010 BASF will be available soon Easier to identify patterns

The Integrated Care Resource Center, a joint initiative of the Centers for Medicare & Medicaid Services Medicare-Medicaid Coordination Office and the Center for Medicaid and CHIP Services, provides technical assistance for states coordinated by Mathematica Policy Research and the Center for Health Care Strategies.

February 9, 2012

For audio, dial: 1-800-273-7043; Passcode 596413

Jim Verdier, Mathematica Policy Research

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Overview

Module 1: Historical Part A/B data

Module 2: Daily/weekly Part A/B data

Module 3: Part D data

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Beneficiary Annual Summary File (BASF)

Contains measures on utilization and expenditures per year by beneficiary

Includes diagnoses from CMS Chronic Condition Warehouse

Part A/B/D claims/event/eligibility data files

Contains data per service paid per beneficiary, as well as demographic and service-level identifiers and diagnoses

Not aggregated at individual beneficiary level

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Advantages Limitations

Can be used for program design and planning purposes; to identify overlaps, gaps, and duplication in Medicare and Medicaid coverage; to identify savings opportunities; and for capitated rate-setting

Contains only limited individual claim-level data, so many details of individual service use (number and type of physician visits, for example) are not available

Easier to use than raw Medicare claims data; can be linked to annual Medicaid data at individual level

Does not include Part D prescription drug data

Shorter lead time to start using data Most recent data are for CY 2009; CY 2010 BASF will be available soon

Easier to identify patterns of service use and costs

Data for beneficiaries in Medicare managed care plans are not available

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Advantages Limitations

Can be used to support individual care coordination activities

Data files are large, hard to use

Data are timelier than the BASF summary files

Data may not be current and complete for all services

Includes Part D PDE data (but must be requested separately)

Non-final-action claims sets may need to be unduplicated

Can be linked to Medicaid data at individual level

Privacy Act requirements may limit sharing of data

Price/cost information is not available for Part D data

Data for beneficiaries in Medicare managed care plans are not available

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Program planning (Part A/B)

Care coordination (Part A/B/D)

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Previously, data requests were made through

the Research Data Assistance Center (ResDAC)

or CMS Coordination of Benefits Agreement

(COBA) process

Now, data are available to states through the

Medicare-Medicaid Coordination Office (MMCO)

Available file types

File record layout/data dictionary

Contents of data package

Data transfer details

Required documents and contact information

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MMCO process for Parts A/B (Module 1) or Part D (Module 3) depends on: Type of data requested

Intended use of the data

Whether the state has a dual design contract

COBA process and requirements are the same for all states (Module 2)

Details are in the Data Toolkit

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State Experiences in Accessing

Medicare Data:

Tips and Lessons Learned

Patrick J. Roohan, Division Director

Quality Improvement and Evaluation

Office of Health Insurance Programs

New York State Department of Health

February 9, 2012

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Lesson 1: Know What Different Data

Files Are Available • Part D Pharmacy (Historical & Prospective) • Historical A & B Claims: (NYS: 2007-2010)

1. Physician / Carrier 2. DME 3. Home Health 4. Hospice 5. Inpatient 6. Outpatient 7. SNF 8. Cross Reference File 9. Denominator File

• Prospective A & B Claim Data (“COBA”) ▫ 2011 forward

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PDE Historical

A & B

COBA

(A & B)

NYS Tip: Due to PDE start up in 2006, NYS requested 2007 forward.

Lesson 2: Have an Analytical Plan for

How CMS Data Files Will Be Used • Cover letters to CMS and Data Use Agreements

will require this level of detail

• Part D Pharmacy (PDE)

▫ Justification on how data will be used for care

coordination is required for each of the 25 data

elements on the PDE file

• Historical Part A & B Claim Data does not

require data element specific justification

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NYS Tip: Be consistent between approval letters and DUA’s on planned data usages.

Lesson 3: Identify Key Program Staff, File

Transfer Mechanism and Frequency

• Project Manager, Custodian and Technical

Contact

• New York receives through Connect Direct

▫ PDE: Monthly

▫ Historical (2007-2010)

Finder file sent to CMS prior to receipt

▫ COBA A & B (2011 forward) : Bi-Weekly

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NYS Tip: We aligned our electronic address/destination location with our MMA file.

Lesson 4: There are Multiple Steps of Approval: Cover

Letters, Data Use Agreements (DUA) & Information

Exchange Agreements (IEA)

PDE

• State Letter to CMS

• CMS Approval Letter to State (DUA # included)

• Information Exchange Agreement

• Data Use Agreement

Historical A& B

• State Letter to CMS

• CMS Approval Letter to State (DUA #Included)

• Information Exchange Agreement

• Data Use Agreement

• Encrypted Finder File to CMS

COBA A&B

• Trading Partner Agreement (COBA ID# included)

• Signatory Letter

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NYS Tip: Be prepared for changes that may be necessary before final approval is received.

Lesson 5: Part D Data are Unique in

Structure, Content and Receipt • PDE data received as one large historical baseline

file; monthly files received thereafter.

• Financial and health plan information are not made available as part of the data feed.

• The file layout and each data element is clearly defined and can be found on the ResDAC website under “Data Documentation” ▫ http://www.resdac.org/Medicare/requesting_data_STATE_CoC.asp

• NYS conducted test file transmissions with CMS prior to receiving production data.

• Netting strategies need to be developed.

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NYS Tip: Apply proxy pricing to the NDC level PDE information to approximate cost.

Lesson 6: Historical Data May Need

to be Converted • When received through Connect Direct from

CMS, data files were in mainframe generated

EBCDIC format and needed to be converted to

an ASCII format to create tables.

• NYS used software to make this conversion.

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NYS Tip: Be prepared for what may be a slow and meticulous process of conversion.

Lesson 7: COBA A & B Data Need to

be “Translated” • COBA claims are transmitted in an X12 837I

(Institutional) and 837P (Professional) data

format.

• New York is currently investigating the best

method for translating to usable data formats.

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NYS Tip: Research available translation software options and be prepared for 5010 implementation.

Lesson 8: Linkages to Medicaid

Recipient Information are Necessary • CMS data is received with the Medicare

beneficiary identification number only.

▫ PDE: Health Insurance Claim Number (HICN)

▫ A & B: Beneficiary Claim Account Number

• Linkages with Medicaid enrollment and eligibility

data are subsequent to file receipt.

• Investigate linkages with the MMA file.

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NYS Tip: The relationship may be “many to many”.

Lesson 9: A & B Claim Data

Dictionary is Difficult to Navigate • NYS is preparing it’s own “Metadata” for

interpreting the data elements received on the

historical claim data.

• ResDAC has a Medicare Data Documentation

link: http://www.resdac.org/ddvh/index.asp

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NYS Tip: Download full document from ResDAC site then use the “Find” function.

Lesson 10: Patience and Persistence

• The process of requesting Medicare data files is a multi-step process that requires frequent communication between CMS, program staff and technical support.

• The data files will require manipulation for conversion to usable data tables.

• States will need to be adaptable to the elements that are not included in the CMS data files: PDE financial and health plan data; Part C capitation dollars.

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• Patrick Roohan: [email protected]

• Mary Beth Conroy: [email protected]

Division of Quality Improvement and Evaluation

Office of Health Insurance Programs

New York State Department of Health

Albany, New York

Phone: 518-486-9012

Fax: 518-486-6098

Contact Information

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23 DSHS | Planning, Performance and Accountability ● Research and Data Analysis Division ● FEBRUARY 9, 2012 (Mancuso)

Data Integration and Predictive Modeling: Supporting Health Interventions

For High-Risk Dual Eligibles

David Mancuso, PhD

WA State Department of Social and Health Services

Research and Data Analysis Division

[email protected]

mailto:[email protected]


0

94 per 1,000 member months


Low Medical Risk High Medical Risk* 0



Low Medical Risk High Medical Risk*

Outpatient Emergency Room and Emergency Room related inpatient utilization among non dual Medicaid Disabled clients

STATE FISCAL YEAR 2009

*High medical risk is defined by a medical risk score of 1.5 or above using Integrated Client Database risk indicators.

SOURCE: DSHS Research and Data Analysis Division, Integrated Client Database, January 2012.

Outpatient Emergency Room Visits ER-Related Inpatient Medical Admissions


Service need and risk factor overlaps among HIGH MEDICAL RISK non dual Medicaid Disabled clients



AOD + SMI = 2,962

12%

AOD + LTC = 769

3%

SMI + LTC = 1,550

6%

AOD + SMI + LTC = 941

4%

SMI+DD 789

3%

SMI + LTC = 47 <1%

DD + SMI + LTC = 24 <1%

GRAND TOTAL

ALL HIGH/MED RISK (Dotted Outline) = 24,006

AOD ONLY = 2,516

10%

SMI ONLY = 2,542

11%

LTC ONLY = 2,733

11%

DD ONLY = 1,988

8%

TOTAL AOD = 7,281

30%

TOTAL SMI = 8,867

37%

TOTAL DD = 2,941

12%

TOTAL LTC = 6,068

25%

Shaded Area Between Dotted Outline and Circles = 7,052

29%


Service need and risk factor overlaps among HIGH RISK DUAL ELIGIBLE Aged or Disabled clients



GRAND TOTAL

ALL HIGH RISK DUAL ELIGIBLES (Dotted Outline) = 44,608

Shaded Area Between Dotted Outline and

Circles = 4,228

9%

TOTAL LTC = 35,411

79%

TOTAL SMI = 12,390

28%

TOTAL AOD = 3,191

7% TOTAL DD

= 2,608

6% AOD ONLY = 641

AOD + SMI = 844

SMI + DD = 1,208

LTC + AOD + SMI = 816

SMI ONLY = 1,356

3%

DD ONLY = 877

LTC + SMI + DD = 138

LTC + DD = 329

2%

LTC + SMI = 7,985

18%

LTC ONLY = 25,296

57%

1%

<1%

3%

LTC + AOD = 834

1% 2%

2%

2%


9%

37%

57%

71%

76%

84%

0%

Utilization-Based Predictive Models Testing samples to assess “out of sample” performance of predictive models calibrated to Medicaid-only Disabled or Aged clients

PROPENSITY SCORE

0 - < 0.25

0.25 - < 0.50

0.50 - < 0.75

0.75 - < 0.90

0.90 - <0.95

PROPENSITY SCORE

0.95 - 1.00

n = 163,606 n = 15,170 n = 4,903 n = 1,603 n = 396 n = 517

3%

40%

53%

66%

72%

81%

0%

PROPENSITY SCORE

0 - < 0.25

0.25 - < 0.50

0.50 - < 0.75

0.75 - < 0.90

0.90 - <0.95

PROPENSITY SCORE

0.95 - 1.00

n = 179,456 n = 3,440 n = 1,437 n = 757 n = 305 n = 800

ER-Related Inpatient Medical Admission in next 12 months

3+ Avoidable ED Visits in next 12 months Non-Emergent or Primary Care Treatable


Medical Hospital Admissions, SFY 2010

Medicaid-only ABD readmission and SNF entry rates by LTC and DD service setting at time of prior hospital admission

NOTE: Unit of observation is an admission in SFY 2010. Clients may have multiple admissions.

Long Term Care and Developmental Disability Service Setting at Time of Initial Hospital Admission

DD Personal Care at Admission

DD Community Residential at Admission

Admitted to Hospital from Skilled Nursing Facility

Long Term Care Adult Residential Care at Admission

Long Term Care Adult Family Home at Admission

Long Term Care Assisted Living at Admission

Long Term Care In-Home Personal Care at Admission

NOT Long Term Care or Client with Developmental Disability at Admission

Number of admissions 24,473 3,538 104 258 159 1,146 162 670

Average age 43.2 50.8 56.5 53.3 53.9 52.7 34.0 21.3

Average Diagnosis/Rx score (1 = SSI AVG) 3.7 5.4 4.9 5.8 4.0 7.0 3.9 4.0

Percent with CCM level of Risk (Diagnosis/Rx score of 1.5+) 76% 93% 99% 96% 92% 98% 86% 77%

Percent with Psychotic Disorder Diagnosis 6% 7% 13% 19% 33% 10% 25% 3%

Percent with Substance Use Disorder Diagnosis 37% 29% 29% 41% 41% 33% 2% 3%

Percent with Hospital readmission within 30 days 20% 25% 21% 19% 32% 27% 17% 14%

Percent with Hospital readmission within 60 days 29% 36% 31% 33% 40% 40% 28% 21%


Antipsychotic Prescriber Quality Indicator Report

Measures include: • Identification of clients currently

“off their meds” • Overall medication possession

ratio with empirically derived threshold defining “good performance”

• Proportion of patients dosed above FDA max

• Polypharmacy review flags • Generic utilization rate


Clients with lower MPR have higher ER and inpatient utilization

Analysis of antipsychotic MPR and “concurrent” ER and Inpatient utilization Study period: April 2010 – March 2011

Medication Possession Ratio Patients OP ER/ED Visits Medical Inpatient Admits Psych Inpatient Admits

Per 1000 MM Per 1000 MM Per 1000 MM

0% < MPR <=40% 2,278 287 31.1 18.1

40% < MPR <=70% 2,026 246 26.5 18.3

70% < MPR <=80% 844 236 29.8 16.6

80% < MPR <=90% 1,190 215 24.1 16.5

90% < MPR <=95% 921 183 21.5 9.9

MPR = 95% or above 5,709 146 18.1 6.2

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Answers to questions that cannot be addressed due to time constraints

will be posted online after the webinar.

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Established by CMS to advance integrated care models for Medicaid beneficiaries with high costs and high needs

Provides technical assistance (TA) to help states integrate care for: (1) individuals who are dually eligible for Medicare and Medicaid; and (2) high-need, high-cost Medicaid populations via health homes as well as other emerging models

TA coordinated by Mathematica Policy Research and the Center for Health Care Strategies

Visit www.integratedcareresourcecenter.com to submit a TA request and/or download resources, including briefs and practical tools to help address implementation, design, and policy challenges

http://www.integratedcareresourcecenter.com/

Documents

Jim Verdier, Mathematica Policy Research€¦ · Shorter lead time to start using data Most recent data are for CY 2009; CY 2010 BASF will be available soon Easier to identify patterns