vhcures: multi-source analysis of the health care spend

Day 2: Track 3 – Measuring Care, Quality and Outcomes with Data

VHCURES: Multi-Source Analysis of the Health Care

Spend

Amy Kinner, MS, Onpoint Health Data

1


All-Payer Claims Database Market

Existing Mandatory APCD (12)

Implementing Mandatory APCD (5)

Strong Interest in Developing APCD (21)

Existing APCD with Voluntary Submission (1)

Existing Voluntary APCD Effort (4)

No Current APCD Activity (7)

2


What Are All-Payer Claims Databases (APCDs)?

• APCDs integrate all claims from all payers • Commercial, Medicaid, Medicare• Pharmacy, Medical, Dental• Enable analyses of cost and health use and quality across all

insurers

3


Vermont’s APCD

• The Vermont Health Care Uniform Reporting and Evaluation System (VHCURES)

• Data collection required by Vermont law• Medicare data provided by CMS• Onpoint-built value-adds required for Vermont Blueprint for

Health analyses (e.g., 3M Clinical Risk Groups, HealthPartners’ Total Cost of Care, etc.)

• Integrates Commercial, Medicaid, and Medicare data• De-identified data• Members tracked through time by unique member_id

4


Vermont’s APCD

Examples of Claims Data Collected• Diagnoses• Procedures• Prescriptions• Date of Service• Cost of service (paid by insurer and patient)• Place of Service• Provider• Patient Demographics

5


Blueprint Clinical Registry

• Data collection at primary care practices in registries or EMRs• Demographic information on patients• Conditions• Variety of clinical measurements• Programmatic data (e.g., Community Health Team work)• Integrated in a central repository

6


Blueprint Clinical Registry

Examples of Patient Data Collected• Body Mass Index (BMI)• Weight• LDL• HDL• Tobacco Use• Hemoglobin A1c• Blood pressure• Vaccinations• Date of visit• Participation in health improvement programs

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Intensive Work to to Improve the Clinical Registry

• The VT Blueprint and Capitol Health work intensely with practices in “sprints” to improve data quality and reporting

• Develop team• Assess data elements being captured at the site• Help site map elements to registry data structure• Continuously improve data submission processes and quality of

data

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Linking Clinical Registries with APCDs

Clinical Registry

Participation in health improvement programs

Data specific to care received in primary care settings

APCD

Primary care measurements (e.g., blood pressure readings)

Care across settings

Cost of care

Diagnoses

Prescriptions

Procedures

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The Value Equation of Data Linkage

Clinical Data All-Payer Claims Data

Enhanced Analytics

10


How to Link Clinical & Claims Data

Use Identical Hashing Methods Across

Databases

Use Common Patient Identifiers to Link…

First Name

Last Name

DOB

ZIP Code

Gender

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FY15 Data Linkage Results

83% of Vermont Blueprint patients with data in VHCURES have been linked to an identifier found in the clinical registry

48% of Vermont Blueprint patients with data in VHCURES had some data collected in the clinical registry during the most recent measurement year (FY15)

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Linking Claims & Clinical Data Sources

13

Patients Attributed to Medical Home Practices – Claims Data (358,117)

Linked to Clinical Registry ID (296,961) Unlinked (61,156)

Measures in FY 2015 (171,134) No Measures (125,827)

Measure # of Patients with DataWeight 153,374Blood pressure 151,478BMI 131,603Triglycerides 43,086LDL-C 40,675Tobacco use 27,493HbA1c 23,059

Examples of Patient Volume for Key Measures

NOTE: Counts represent dates of services on and between 7/1/2015 and 6/30/2015

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Practice and Regional-Level Profiles

Publicly available Vermont Blueprint HSA Profiles

blueprintforhealth.vermont.gov/node/680

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Hypertension, Blood Pressure in Control

Proportion, including 95%-confidence intervals, of continuously enrolled members with hypertension, ages 18–85 years, whose last recorded blood pressure measurement in the clinical database was in control (<140/90 mmHg); the blue dashed line indicates the statewide average

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Diabetes HbA1C Control & Outcomes

Metric HbA1c in Control * HbA1c Not in Control *

Members 4,220 568

Average annual expenditures per capita

$12,507 ($12,059, $12,954)

$15,267 ($13,867, $16,667)

Inpatient hospitalizations per 1,000 members

181.7 (168.7, 194.7)

275.0 (231.1, 318.8)

Inpatient days per 1,000 members

877.8 (849.2, 906.4)

1,524.0 (1,421.8, 1,627.2)

Outpatient ED visits per 1,000 members

532.1 (509.8, 554.4)

725.2 (654.0, 796.4)

* Risk-adjusted rates and 95% confidence intervals; 99th percentile outliers excluded; HbA1c not in control >9%, in control ≤9%

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Data for Use in a Learning Health System

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Measurement

Expenditures

Quality

Utilization

Patient Experience

Unit Costs

Variations & Associations

Social, Economic, Behavioral Outcomes

ProductsProfile Reporting

Program Evaluations

Predictive Modeling

Learning System Support

Peer-Reviewed Publications

Payment Performance Analytics

© Onpoint Health Data ∙ Confidential & proprietary information ∙ Not for redistribution

Clinical Registry

BRFSS Data

CAHPS Data

Corrections data

Other?

Provider Registry

Claims data from APCD

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Contact Information

Amy Kinner: [email protected], 207-430-0650

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VHCURES: Multi-Source Analysis of the Health Care

Spend

Mary Kate Mohlman, PhD, MSVermont Blueprint for Health

Department of Vermont Health Access


Vermont Blueprint for Health• Supports practice transformation to patient-centered medical

homes (PCMHs)• Supports multi-disciplinary community health teams integrate

with PCMHs to coordinate social and medical services• Transformation Network:

o Practice facilitators: work with practices on meeting NCQA standards and developing quality improvement initiatives

o Project managers: who work to implement program initiatives and engage community partners

o CHT leaders: coordinate CHT activities with PCMHs

• Use of statewide multi-payer claims and clinical data to evaluation overall health services performance and programmatic components, to support payment reforms, and to provide feedback for a Learning Health System


Data Quality

• Outcomes/analysis only as good as the data used• Data Quality Projects

o Goal: to ensure accurate, timely, and reliable end-to-end data extraction, transmission, and registry reporting to support the delivery of high quality health services

o Team tailors work to needs of independent PCMHs or hospital/health systems

o Completion when lean clinician and project team representative verify continuity of data quality from source EMR to VHIE to clinical registry

• Increasing number of patients with clinical measures supports meaningful reporting, accurate comparative analysis, and more complete linked claims and clinical data


0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

Percent of practices in service area with >80% of patients with clinical measures in the clinical registry

Jul 2013-Jun2014 Jan 2014-Dec 2014


264,554

301,209 305,051296,961

112,388

137,627

162,118 171,134

42.5%45.7%

53.1%

57.6%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

Calendar Year 2013 State Fiscal Year 2014 Calendar Year 2014 State Fiscal Year 2015

Growth in Linked Claims and Clinical Records Containing Clinical Measures

VCHURES IDs linked to Clinical Registry IDs Linked records containing clinical measures Percent Linked


Conversation Starter

Prac

tice

Prof

ile


Linked Data Use in Profiles


Statewide Population Health Evaluations

• Value of whole populationo Large numbers – better able to identify statistically significant

associations Retrospective analysis Modeling for projected trends

o Comparative analytics Compare outcomes for PCMH-attributed patients to non-PCMH Compare outcomes for ACO covered lives versus non-ACO Better understanding how different payers are meeting the needs of

their specific populations Addressing variability in health services utilization and expenditures

across state regionso Evaluating statewide policies

Evaluating policy effectiveness Supporting decisions for future policies


Statewide Population Health Evaluations

• Diabetes: reviewing link between HbA1c, common comorbidities, and near-term expenditures and utilization patterns

• Hypertension: reviewing links between hypertension diagnosis, blood pressure and cholesterol treatment, comorbidities, and expenditures and utilization patterns.

• Predictive Analytics: generating projections for policy recommendations under alternative payment models

• Linking with other Statewide datasets o Correctionso Meals on Wheels


Discussion

• Questions? Comments?

• Contact informationMary Kate [email protected]

mailto:[email protected]

vhcures: multi-source analysis of the health care spend

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