ACO Quality Measure Reporting

ACO QualityMeasure ReportingStrategies for a Successful CMS Medicare Shared Savings Program

1November 14, 2013

Verisk Health Webinar Series

Presenter: Lynne Rothney-Kozlak, MPH

Lynne Rothney-Kozlak, MPH, is President and Principal of Rothney-Kozlak Consulting, LLC. She is also an Adjunct Instructor of Public Health with the University of New England, College of Osteopathic Medicine.

She has led NIH epidemiology research at Yale and has been the CEO of a statewide public health institute. She was a senior managed care executive with a large regional health plan and with a provider-sponsored health plan.

Lynne received her MPH from Yale and has over 25 years of experience with the public, private, academic and non-profit sectors leading strategic planning, population health management, quality measurement, provider performance evaluation, developing technology solutions and health informatics.

Her executive experience is applied to her consulting practice. She supports organizations in their strategic, technical and business development including Premier’s Partnership for Care Transformation (Accountable Care Collaboratives), NCQA’s measure specifications for health plans, physicians and accountable care organizations, Booz Allen Hamilton's deployment of Cloud Analytics in healthcare and Verisk Health’s analytics product development for at-risk provider measurement and population health management.

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Rothney-Kozlak Consulting, LLC

Outline of Session

Accountable Care and Risk Management• Reimbursement shifts and population management

Requisite Informatics and Measurement• Informatics capabilities and measurement considerations

CMS MSSP / Pioneer Quality Measurement• CMS reporting requirements and data collection processes

Strategies for the Future• Five key strategies to consider

Summary and Discussion

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Accountable Care andRisk Management

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Lines Are Blurring Across Payers and Providers

Physicians

Aggregate Customers

Finance Healthcare

Coordinate Care

Prevent Disease & Promote Wellness

Deliver Outpatient

Care

Deliver Inpatient

Care

Deliver Post Acute

& Long Term Care

Payers

IDNs

Traditional boundaries between health care

financing and delivery are increasingly being crossed

Key Population Health Value Chain ComponentsPlan Design & Financing Wellness & Coordination Care Delivery

Financial Risk Care Delivery Risk

Slide Content Courtesy of Premier, Inc. 5

Risk Shifting to Providers in Various Forms• Shift from FFS to capitation will not happen overnight – financial / actuarial / clinical

infrastructure critical

• Providers will likely need to navigate multiple types of payments over the next 5 years

• Local market dynamics, degree of clinical integration, benefit plan design and patient population (i.e. commercial, Medicare) are all key factors

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Forms of payment transformation

Fee for Service

Pay for Performance

Bundle / Episodic Payment

Global Budgets / Shared Savings

Partial / Global Capitation

Provider Payment Models

Virtual community networks

IPAs / MSGs PHOs Payer / Provider Organizations

Fully-integrated delivery systems

Provider Practice Models

Accou

ntabil

ity sh

ifts

close

r to ca

re deliv

ery

The Shared Savings Model

-3 -2 -1 0 1 2 3

Exp

end

ing

Year

Projected Spending

Actual Spending

SharedSavings

Target Spending

ACO Launched

Source: Lewis, Julie. “What Could be Next for Health Reform? The Debate In Washington” Presentation. The Dartmouth Institute for Health Policy & Clinical Practice. 2009-07-02.


Accountable Care Market Segments

Employee Health Plan

Self-funded Employers

Private Health Plans

Medicaid Program

Medicare Program

Uninsured

Retail Health Insurance

8Slide Content Courtesy of Premier, Inc.

…and People are Getting on Board…

Stage 1 Stage 2 Stage 30

20,000

40,000

60,000

80,000

100,000

40,000

+20,000

+20,000

Sample Health System Contract

Commercial Risk Con-tract

MSSP ACO Contract

Employees

Nu

mb

er o

f L

ives

Growth and Dispersion of Accountable Care Organizations: June 2012 Update. Leavitt Partners.

Single Provider ACO67%

Multiple Provider ACO19%

Insurer ACO8%

Insurer-Provider ACO6%

Types of ACOs

• Provider entities are the majority of ACO sponsors

• Growth in risk contracts tends to come in waves

Success Under Risk Requires attention to leakage, utilization, and outcomes...…in that order of priority

Manage leakage

Inpatient referrals

OP procedural referrals

OP non-procedural referrals

Imaging

Primary care

Manage utilization

Discretionary procedures

Post acute care

End of life care

High cost imaging

Pharmacy

Improve patient outcomes

Admission/ readmission

reduction

Rx compliance

Patient access

Chronic conditions

Cancer case management


Case Management

Disease Management

Prevention

Well & Low Risk Members

(Prevention)

Low Risk Members (Prevention and

Disease Management)

Moderate Risk Members (Disease

Management)

High Risk, Chronic, Multiple Disease

States (Episodic Case Mgmt- Inpatient

Clinical Guidelines)

Complex Catastrophic Care

(Inpatient - LTC)

End of Life

Increasing Health Risk

Decreasing Health Risk

1 2 3 4 5

Population-Based Care Management Framework

Source: Paul H. Keckley, Executive Director, Deloitte Center for Health Solutions, Washington DCPhD, 2007 National Predictive Modeling Summit: The Landscape for Predictive Models

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Requisite Informatics andMeasurement

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Business & Clinical IntelligenceMaturity Model

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Stages of BI Maturity

In order to achieve an optimum future state of business intelligence and management reporting, organizations will mature through various stages

Beginning

Developing

Defined

Advanced

Leading

Spreadsheet driven Significant manual effort

to collect data Limited to operations and

regulatory reporting Limited knowledge of data

sources

Ad hoc models / spreadsheets

Automated reporting limited to transactional systems

Many operational performance measures have definitions, but different values are reported

Organization has a formal BI strategy

Data is gathered from disparate systems

Some integration across business units

Improved information access and delivery

Subject area data warehouses

Developing data governance processes

Information integration across organization

Enterprise data warehouse, including robust metadata repository

Processes exist to integrate additional data sources and domains

Single version of truth Formal data

governance requirements and policies

Personalized dashboards and alerts

Near real-time performance monitoring

Forward looking analytics, forecasting and predictive models

BI competency center to maintain strong governance

Source: Deloitte

Data Fuels Population Health Management

1. Adjudicated Claims from a TPA (employer), PBM or Payer• Includes both medical and pharmacy claims• Provides insight into patients’ experience outside of ACO / health system• Provides financial data to estimate total cost of care and cost trends

2. Health Risk Assessments• Surveys collected through a wide range of instruments and modes• How are repeated assessments captured and managed? • How are disparate survey data integrated for seamless data analysis?

3. Clinical Data – Labs, EHRs, etc. • EHR / lab data from ACO’s employed providers and affiliated providers• Biometric data collected outside of care delivery via wellness programs

4. Disability / Attendance Data• Can presenteeism or absenteeism estimates be calculated? • Can disability program data identify employees for disease management?

14Slide Content Courtesy of Premier, Inc.

Whatever data that is integrated, keep an eye on the long term ACO strategy ball. What do you need to manage risk across populations?

Definition of Success:Improving Triple Aim™ Population Outcomes

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Population Health

Per Capita Costs

The term Triple Aim is a trademark of the Institute for Healthcare Improvement

Experience of Care

Sample Measures– Improving Health

• (e.g. Adult BMI Assessment – an NCQA HEDIS measure)

– Improving Experience• (e.g. Clinician and Group CAHPS:

Shared Decision Making)– Improving Cost (and utilization)

• (e.g. AHRQ’s Ambulatory Care Sensitive Admissions - all 14)

Slide Content Courtesy of Premier, Inc.

Considerations for Selecting Accountable Care Measures

Are the measures…• endorsed by the National Quality Forum?

• representative of the full Triple Aim – service, health and cost?

• applicable for the population(s) being managed?

• valuable to evaluating relevant program and risk contracts?

Do the measures have… • well defined time frames, denominators, numerators, etc.?

• the requisite data available across the entire population?

• feasibility at the population, provider and patient level?

• adequate denominator sizes?

• readily available benchmarks at the regional and national?


Considerations for Complying with Accountable Care Measurement

• Quality measures are nearly always contractually required

• often the “gate” to access shared savings or other incentives

• Managing multiple populations at risk with different measure sets

• Measurement sets vary widely with varying data dependencies

• Ongoing performance monitoring versus contractual reporting

• Integrate measures into overall population management strategies

• Unfortunate gaps in comparable benchmark availability

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CMS MSSP / Pioneer Quality Measurement

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Measure Category Number of Measures Measure Steward Measure (abbreviated names)

Preventive Health (8 Measures)

3 NCQA (2 HEDIS measures) Colorectal & Breast CA Screening; Pneumococcal Vaccine

3 CMS Adult Weight , Depression & Blood Pressure Screening

2 AMA-PCPI Influenza Immunization; Tobacco Use Assess / Cessation

At Risk Population(12 Measures)

5 MN – Community Measurement DM A1c, LDL, BP Control, Tobacco non-use & Aspirin Use

4 NCQA (2 HEDIS measures) DM A1c Poor Control; HTN BP Control; IVD LDL Control, Use of Aspirin

3 CMS / AMA-PCPI HF Beta-Blocker for LVSD1; CAD Rx for LDL control, ACE or ARB CAD and DM and/or LVSD

Patient/Care Giver Exp(7 Measures) 7 AHRQ Clinician & Group CAHPS Survey: Composites of 80+ Qs

Care Coordination / Patient Safety(6 Measures)

2 AHRQ ACSC Ambulatory Sensitive Conditions Admissions: COPD & HF

1 CMS PCP EHR Incentive Program Reporting (Meaningful Use)

1 CMS Risk-Standardized All-Cause Re-Admission

1 NCQA (not a HEDIS measure) Medication Reconciliation after Discharge from IP Facility

1 AMA-PCPI/ NCQA Screening for Fall Risk

Shared Savings

1 Left Ventricular Systolic Dysfunction


Measuring the Triple Aim: CMS Final Rule – 33 MSSP Measures

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CMS MSSP Quality Measurement Reporting Cycles

Source: http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram/Downloads/ACO-Guide-Quality-Performance-2012.PDF


http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram/Downloads/ACO-Guide-Quality-Performance-2012.PDF



CMS MSSP Quality Measurement Scoring Rubric

Table 2. Sliding Scale Measure Scoring Approach

Source : http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram/Downloads/2012-11-ACO-quality-scoring-supplement.pdf


http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram/Downloads/2012-11-ACO-quality-scoring-supplement.pdf




Breaking Down Modes of Data Collection: The “CMS MSSP-33”

1 EHR meaningful

use measure

3-claims only measures

7 CG-CAHPS measures

22 hybrid measures

CMS collection process

Provider reports at the TIN level; CMS compiles

CMS calculates from CMS data

CMS and ACO collaborate using GPRO tool

CMS administers on a sample of attributed members*

Steps required for periodic member collection

No net new; Provider reports at the TIN level, compile

ACO calculates from claims given by CMS

Calculate denominators from claims, select sample, manually abstract hybrid elements, run final dataset through rules engine

Commission periodic surveys on member samples

*Note: CMS will only collect survey data on behalf of their shared savings contractors for reporting period 2012 and 2013; so starting with the reporting period 2014 the contractors need to hire a survey vendor

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Key assets required

None Claims engine ‘GPRO-like’ toolMSSP-33 engine

Survey administration


A MSSP GPRO Example: Breast Cancer Screening Data Collection

Step 5: CMS will conduct random audits of GPRO data and then compile an overall Breast Cancer Screening rate for the ACO along with MSSP benchmark information.

Step 1: CMS identifies from the ACO’s attributed beneficiaries, all women who are eligible for the measure based on the measure specifications (age, enrollment, exclusions, etc.) using their own demographic (enrollment) and claims data.

Step 2: CMS draws a random sample with an oversample (for 616 or the entire eligible population) from that pool of eligible female beneficiaries, which creates the measure denominator. For the (8) PREV sample modules (including this measure) CMS tries to overlap beneficiary samples between measures to reduce ACO data processing and collection burden.

Step 3: CMS will then send to each ACO, via the GPRO tool, the entire sample for this measure (matched to other module samples). Each sampled beneficiary is rank-ordered from 1 to 616 to guide the prioritization of ACO data collection, which must be submitted in a sequential order.

Note: While CMS provides measure specifications as to what satisfies the numerator and denominator exclusions, multiple sources and documents are used to convey these requirements.

Step 4: The ACO has 8 weeks to electronically or manually scour their medical records for those beneficiaries assigned by CMS, looking for evidence of a mammogram in the last two years and following explicit requirements. The ACO looks for data across their EHR systems (electronically) and/or their provider network (manually) to complete up to 411 sequential sampled beneficiaries. Through GPRO, the ACO submits data either record by record or through XML file up-loads.


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CMS / CMMI Webinar on Quality Reporting: Lessons Learned and Shared Experiences, June 27, 2013

From Cumberland Center for Healthcare Innovation, LLC (TN)

Issues with the Collection Process

• Wrong versions of Excel – needed 2007 or better

• Lots of questions about the pre-populated data

• Unable to verify “not qualified for sample” entries

• Unable to verify “confirmed status” across all measures

• Unable to verify dates of services

• Unable to verify missing entries by practice

• Last minute submissions by some of our practices

• Ranked submission requirement limited editing of early submissions

• Higher rate of “not confirmed” than anticipated – 2 categories >10%?

• Several “outage” periods during the collection process

• Slow performance of the GPRO interface during normal business hours


Strategies for the FutureSupporting ACO measurement and performance

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Five Key Strategies to Consider

1. Integrate and collect disparate data into consolidated business intelligence

2. Programmed tools to support MSSP GPRO data collection processes

3. Compliance with ACO quality measure abstraction and submission to CMS

4. Ongoing quality measurement to monitor performance and improvement

5. Population health management to improve cost and quality performance

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1. Integrating and Collecting Disparate Data

Beneficiary and Medicare A, B, D Claims

• Incorporate CMS eligibility, A-B-D claims data

• Cross-walk ACO provider network files with claims and eligibility data.

Medical Record Retrieval

• Chase logic to identify targeted medical record sampled beneficiaries’ medical records by measure

• Provider communication to retrieve targeted charts from various sites for centralized abstraction

EHR Data Feeds

• Incorporate EHR data extracts using a robust data model with coding standards

• Integrate EHR data such that it can electronically satisfy requisite clinical data reporting for CMS

Medical Chart Abstraction

• Accurate measure-specific clinical data abstraction forms integrated within a data platform

• Easy, remote access by many concurrent abstractors for rapid data abstraction from clinical sites

• Integration of abstracted data into measure calculations, status reporting and submissions

Survey Results

• Incorporate CG CAHPS results from CMS or the ACO’s survey vendor by population or provider

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2. Tools to Support MSSP GPRO Data Collection

ACO Pain Points from 2013

• Manual data entry into GPRO required for 30 - 100% of sampled records, depending upon sophistication and integration of EHR data

• Issues managing the project during submission

• Poor progress reporting in GPRO

• GPRO reports will not pull full rate if a member in sequence was not done yet

• Inability to download reports that can be manipulated in Excel or other databases

• Instructions on how to use GPRO are vague

• Measure specifications are very difficult to integrate across many CMS documents

• Changes to specifications were difficult to keep track of through the CMS Q/A process.

• GPRO is only an abstraction tool, open for use only for annual submission. So it does not offer monitoring services throughout the year

• Limited access at one time - 10 users - hard to manage if all licenses went to reviewers

• GPRO technical performance is weak

Solutions of the Future

• Strong process for supporting required clinical data collection and submissions

• GPRO auto-filled fields are uploaded into QI

• Clinical data integrated into QI from EHR feeds to automatically satisfy applicable GPRO fields

• Chase logic for efficient tracking of missing data across the provider network – finding the records

• Integrated abstraction forms with detailed, up-to-date instructions from CMS specifications; integrating rule sets across multiple sources

• “one button” GPRO XML file generation

• Data collection progress reporting, based on years of HEDIS experience

• Ample licenses for access to QI

• Year-round access to data to monitor performance and benchmarking

Provider Chase Logic Configuration

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GPRO Module Selected Chase Logic (Short Desc)

Verisk Health Default Chase #

Customer Chase # Max

Max by

ChaseMax by

Provider Selected Chase Logic (Long Description)

CARE 1

ACO 12: Medication Reconciliation

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PCP most m- year 53 53 1 1 PCP with the most visits in the measurement yearCard recent visit 4 4 1 1 Cardiologist with most recent visitEndo most m- year 7 7 1 1 Endocrinologist with the most visits in the measurement yearPCP assign at end of m- year 43 43 1 1 PCP assigned as of the end of the measurement yearFac of another admit m- year 11 11 1 1 Facility of another admission in measurement year

CARE 2ACO 13: Falls: Screening for Future Fall Risk

3

PCP most m- year 53 53 1 1 PCP with the most visits in the measurement yearProvider most m- year 69 69 1 1 Provider with most visit during measurement yearOB/G most m- year 34 34 1 1 OB/GYN with the most visits in the measurement yearFac of another admit m- year 11 11 1 1 Facility of another admission in measurement year

PREV

ACO14: Preventive Care and Screening: Influenza Immunization

3

PCP most m- year 53 53 1 1 PCP with the most visits in the measurement yearCard most m- year 2 2 1 1 Cardiologist with the most visits in the measurement yearOB/G most m- year 34 34 1 1 OB/GYN with the most visits in the measurement yearPCP most w/ imm codes 60 60 1 1 PCP with the most visits containing immunization codes

By carefully building provider chase logic designed for each quality measure, the manual data collection process is streamlined and focused.

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Integrated Data Abstraction – MSSP Diabetes

3. Compliance with CMS ACO Quality Measures

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Considerations for Reporting Accurately

• Have current CMS specifications tracked by experts to ensure accurate e-programming and maintenance of abstraction forms

• Ensure accurate patient exclusions such as for demographic data errors

• Maintain CMS sample ranking across all modules to ensure complete abstraction

• Track progress by one or all sample modules, including skip rates thresholds

• Have data abstraction quality controls in place such as valid value limits on forms

• Ensure only clinical data (electronic or abstracted) are used to populate numerator fields for ACO GPRO XML file submissions

CMS GPRO Data Submission ProcessJune – December

• CMS publishes various documents that together define GPRO measure specifications

• All told, there are 7 or more such sources, which makes it very difficult to decipher the rule sets

January

• CMS MSSP GPRO XML file downloads to organize data for abstraction support

January – March (1/27/2014 – 3/21/2014)

• Satisfy numerator requirements using electronic clinical data

• Facilitate data collection process for balance of numerators using accurate abstraction forms

• Generation of Interim uploads to GPRO using the XML file format

By March Deadline

• Final XML file upload to GPRO for CMS submission

Measure Rate Summary – Tobacco Use

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Population: Adjudicated Claims and Electronic Clinical Data

Data Elements General Measure Data

Full Beneficiary Population 15,698

Patients Compliant through Adjudicated Claims Data 6,845

Patients Compliant through Electronic Clinical Data 1,329

Reported rate 52.07%

Lower 95% confidence interval 51.29%

Upper 95% confidence interval 52.86%

Sample: Electronic and Abstracted Clinical Data (for CMS Submission)Data Elements General Measure Data

CMS Total Sample Including Oversample 616

Denominator Exclusions 35

Final Denominator for Analysis 411

Patients Compliant through Clinical Data Only 265

Reported rate 64.48%



Sample: Adjudicated Claims and Clinical Data (Electronic and Abstracted)

Data Elements General Measure Data

Final Denominator for Analysis 411

Denominator Exclusions 35

Patients Compliant through Claims Data Only 112

Patients Compliant through Clinical Data Only 265

Reported Rate 91.73%



ACO 17 (GRPO PREV-10) (NQF #0028): Preventative Care and Screening: Tobacco Use: Screening and Cessation Intervention

Measurement Year: 01/01/2013 - 12/31/2013

Refresh Date: 09/18/2013

Description:

Percentage of patients aged 18 years and older who were screened for tobacco use one or more times within 24 months AND who received cessation counseling intervention if identified as a tobacco user.

Three different rates are produced for each MSSP GPRO measure: segregating data and then combining data sources for different purposes (reporting vs. monitoring).

4. Ongoing Quality Measurement Monitoring

Imbed Benchmarks in Reporting• Aggregate of all at-risk populations managed by the ACO

• 5% FFS Medicare sample

• CMS’s MSSP Quality Measure benchmarks when available (or summary statistics from 2012, for now)

Comparative Trending • For year-to-year tracking incorporate ACO’s prior year results for comparison today

• Ability for users to flex reporting periods for interim monitoring (e.g. off calendar year, rolling 12 month periods)

• Dashboards to display and trend 33 CMS MSSP measures annually reported by CMS and interim internal monitoring

Proxy Claims Measures for Clinically Dependent Measures• Allow for adjudicated claims to estimate CMS MSSP quality measures, where such an estimate is meaningful

• Use proxy measures that identify gaps in care related to the MSSP quality measures to easily track opportunities

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Available Benchmarking

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Summary of Measure Performance

Reports will be available to monitor all MSSP measures comparing the ACO’s performance with varying rates and goals, along with CMS summary statistics

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Related Verisk Health Quality Measures for Action - Diabetic Care

ACO Measures: #22-27 Composite Measures for Diabetes Mellitus

Corresponding number of Verisk Health Quality and Risk Measures (QRMs): 53

Samples:

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QRM #Model Type Condition Description

3063(E) Gap Diabetes (E) Patients without HbA1c test in the last 12 months.

8855 Gap Diabetes Patients taking insulin and sulfonylureas at the same time.

8836(E) Gap Diabetes taking insulin in the last 12 months (E)

Patients without home glucose measurement devices in the last 12 months.

8661(E) Gap Diabetes-related admission in the last 12 months (E)

Patients without diabetes-related office visit in the last 12 months.

8574(E) Gap Diabetes-related ER visit in the last 12 months (E) Patients without office visit in the last 12 months.

3401(E) Gap Diabetes + Hypertension + Obesity (E) Patients without antihyperlipidemic drugs in the last 12 months.

3401 Gap Diabetes + Hypertension + ObesityPatients without antihyperlipidemic drugs in the analysis period.

3147 Risk Diabetes Patients with complicated lipid disorders.

3140 Risk Diabetes Patients with hyperlipidemia.

5. Population Health Management … to improve cost and quality performance

• Drill down by provider and patient by measure, for action to improve care

• Track disease management initiatives related to quality measures (e.g. DM)

• Mirror the CMS quarterly utilization and financial reports for drill-down analysis

• Apply advanced risk analytics to enhance a wide array of strategies

• Analyze cost of care and outlier patients and providers

• Identify and manage gaps in care by patients and providers

• Optimize network efficiency and in-network utilization through targeted analyses

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Enabling Population Health Management

Analyze Cost of Care and Outliers• Total per member per month Costs

• Spotting those driving costs

• Procedure, medication, and other details

Identify and Manage Gaps in Care• Patient level (gaps in care, disease registries)

• Provider level (referrals/leakage, PCP level analysis)

Understand Future Risk• DxCG risk models used by payers

• Predictive modeling

• Use across various at-risk populations

• Support shift to value based purchasing

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Mirroring CMS Util. / Cost Report for Drill Down

Medicare Shared Savings Program

Aggregate Expenditure/Utilization Trend ReportACO A1166 <ACO Name>

Year 2013, Quarter 1

ACO-Specific Assigned

Beneficiaries1 ACO Cohort1

Annual AnnualTotal Expenditures per Assigned Beneficiary Medicare Enrollment Type2, 4 Total 9,402 8,979 End Stage Renal Disease 60,180 61,192 Disabled 9,141 8,242 Aged/Dual 14,999 11,986 Aged/Non-Dual 9,086 8,263 Component Expenditures per Assigned Beneficiary3 Inpatient4 3,342 2,959 Indirect Medical Education (IME)4 38 121 Disproportionate Share Hospital (DSH)4 188 295Skilled Nursing Facility 578 663Institutional (Hospital) Outpatient 2,507 1,456Part B Physician/Supplier 2,141 3,013Home Health 352 490Durable Medical Equipment 315 285

Hospice 323 214

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Functionality that 1) will allow drill down by provider and patient for root cause action, and 2) will allow validation of CMS reports

Key Take Home Messages

1. You are not alone – there are common transformation objectives

Many providers are going down the path of taking on population risk management

2. There are a myriad of factors that contribute to risk management success This transformation requires new ways of collecting, managing and analyzing data

3. Taking risk for populations means effectively managing their health Seek external assistance to turn data into information as data is a key foundation

4. Meaningful measures are key to evaluating accountability Understand the complexity of contractually required quality measurement

5. Planning and executing relevant functionality is a critical success factor Have effective informatics and electronic quality measurement strategies

6. Verisk Health is delivering integrated provider products to meet key needs The focus is supporting ACO measurement and performance improvement

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ACO Quality Measure Reporting

Technology

health informatics

health care financing

health solutions

health reform

population health management

providersponsored health

large regional health

risk contracts