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ACO Quality Measure Reporting

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Strategies for a Successful CMS Medicare Shared Savings Program …

Strategies for a Successful CMS Medicare Shared Savings Program


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  • Marina will start, Matt will finish.As Steve mentioned at the beginning of the conference, 1 year ago, our clients were barely talking about risk contracts - today they are on their 3rd and 4th contractsNov 2011, just 1 year ago “Despite the proliferation of ACO activity, no dominant model has emerged. … It is clear that leading organizations across the provider and payer sectors are increasingly committing to experimentation and iteration of risk-based payment models. … Medicare ACOs may have provided the impetus for the ACO movement, but it appears that they may not be the driving force behind accountable care’s continuing development.”Provider organizations are assuming the leadership and riskThen Matt - started with employees etc.
  • Premier opinion: to minimize risk of failure, you need to ensure you develop competence in certain areas first. The BCBSMA AQC experience taught us that simple shifts in referral location can yield a several percentage point reduction in costs – with essentially no change in operations or clinical practice and minimal exertion of resources. With a more sophisticated understanding of internal cost accounting, the opportunity is greater. Discretionary utilization and utilization at critical junctures – post acute and end of life care in particular – also represent high yield opportunities for better management. The ultimate goal is improving patient outcomes, and the following areas have the best evidence in support of impact on cost and outcome improvement, but these changes require the most commitment and take the most time to achieve results.
  • Transcript

    • 1. ACO Quality Measure Reporting Strategies for a Successful CMS Medicare Shared Savings Program Verisk Health Webinar Series November 14, 2013 1
    • 2. Presenter: Lynne Rothney-Kozlak, MPH Rothney-Kozlak Consulting, LLC 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 atrisk provider measurement and population health management. 2
    • 3. 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 3
    • 4. Accountable Care and Risk Management 4
    • 5. Lines Are Blurring Across Payers and Providers Key Population Health Value Chain Components Plan Design & Financing Aggregate Customers Finance Healthcare Wellness & Coordination Coordinate Care Prevent Disease & Promote Wellness Care Delivery Deliver Outpatient Care Deliver Inpatient Care Deliver Post Acute & Long Term Care Physicians IDNs Payers Care Delivery Risk Financial Risk Traditional boundaries between health care financing and delivery are increasingly being crossed Slide Content Courtesy of Premier, Inc. 5
    • 6. 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 Forms of payment transformation Partial / Global Capitation Global Budgets / Shared Savings Provider Payment Models Bundle / Episodic Payment Pay for Performance Fee for Service Virtual community networks IPAs / MSGs PHOs Payer / Provider Organizations Fully-integrated delivery systems Provider Practice Models 6
    • 7. The Shared Savings Model ACO Launched Projected Spending Target Spending Shared Savings Expending Actual Spending Year -3 -2 -1 0 1 2 3 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. Slide Content Courtesy of Premier, Inc. 7
    • 8. Accountable Care Market Segments Employee Health Plan Self-funded Employers Private Health Plans Medicaid Program Medicare Program Uninsured Retail Health Insurance Slide Content Courtesy of Premier, Inc. 8
    • 9. …and People are Getting on Board… • Provider entities are the majority of ACO sponsors • Growth in risk contracts tends to come in waves Types of ACOs Insurer ACO 8% InsurerProvider ACO 6% Multiple Provider ACO 19% Single Provider ACO 67% Number of Lives 100,000 80,000 Sample Health System Contract +20,000 60,000 +20,000 40,000 20,000 Commercial Risk Contract MSSP ACO Contract 40,000 0 Stage 1 Stage 2 Stage 3 Employees Growth and Dispersion of Accountable Care Organizations: June 2012 Update. Leavitt Partners.
    • 10. Success Under Risk Requires attention to leakage, utilization, and outcomes... …in that order of priority Manage leakage Manage utilization Improve patient outcomes Inpatient referrals Discretionary procedures Admission/ readmission reduction OP procedural referrals Post acute care Rx compliance OP nonprocedural referrals End of life care Patient access Imaging High cost imaging Chronic conditions Primary care Pharmacy Cancer case management Slide Content Courtesy of Premier, Inc. 10
    • 11. Population-Based Care Management Framework Increasing Health Risk Well & Low Risk Members (Prevention) Low Risk Members (Prevention and Disease Management) 1 2 Moderate Risk Members (Disease Management) 3 High Risk, Chronic, Multiple Disease States (Episodic Case Mgmt- Inpatient Clinical Guidelines) Complex Catastrophic Care (Inpatient - LTC) End of Life 4 5 Decreasing Health Risk Prevention Case Management Disease Management Source: Paul H. Keckley, Executive Director, Deloitte Center for Health Solutions, Washington DC PhD, 2007 National Predictive Modeling Summit: The Landscape for Predictive Models 11
    • 12. Requisite Informatics and Measurement 12
    • 13. Business & Clinical Intelligence Maturity Model In order to achieve an optimum future state of business intelligence and management reporting, organizations will mature through various stages Stages of BI Maturity Leading Advanced Defined Developing  Ad hoc models / spreadsheets  Automated reporting limited to transactional Spreadsheet driven systems Significant manual effort  Many operational to collect data performance measures Limited to operations and have definitions, but regulatory reporting different values are Limited knowledge of data reported sources Beginning     Source: Deloitte  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 13
    • 14. 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? Whatever data that is integrated, keep an eye on the long term ACO strategy ball. What do you need to manage risk across populations? Slide Content Courtesy of Premier, Inc. 14
    • 15. Definition of Success: Improving Triple Aim™ Population Outcomes 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) Population Health Per Capita Costs Experience of Care The term Triple Aim is a trademark of the Institute for Healthcare Improvement Slide Content Courtesy of Premier, Inc. 15
    • 16. 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? Slide Content Courtesy of Premier, Inc. 16
    • 17. 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 17
    • 18. CMS MSSP / Pioneer Quality Measurement 18
    • 19. Measuring the Triple Aim: CMS Final Rule – 33 MSSP Measures Measure Category Number of Measures Measure Steward 3 NCQA (2 HEDIS measures) Preventive Health Measure (abbreviated names) Colorectal & Breast CA Screening; Pneumococcal Vaccine Influenza Immunization; Tobacco Use Assess / Cessation MN – Community Measurement DM A1c, LDL, BP Control, Tobacco non-use & Aspirin Use 4 NCQA (2 HEDIS measures) CMS / AMA-PCPI 7 AHRQ Clinician & Group CAHPS Survey: Composites of 80+ Qs 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 Care Coordination / Patient Safety AMA-PCPI 2 Patient/Care Giver Exp (7 Measures) Adult Weight , Depression & Blood Pressure Screening 3 (12 Measures) CMS 5 At Risk Population 3 2 (8 Measures) AMA-PCPI/ NCQA Screening for Fall Risk DM A1c Poor Control; HTN BP Control; IVD LDL Control, Use of Aspirin 1 HF Beta-Blocker for LVSD ; CAD Rx for LDL control, ACE or ARB CAD and DM and/or LVSD (6 Measures) Shared Savings 1 Slide Content Courtesy of Premier, Inc. Left Ventricular Systolic Dysfunction 19
    • 20. CMS MSSP Quality Measurement Reporting Cycles Source: http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram/Downloads/ACOGuide-Quality-Performance-2012.PDF Slide Content Courtesy of Premier, Inc. 20
    • 21. CMS MSSP Quality Measurement Scoring Rubric Table 2. Sliding Scale Measure Scoring Approach Source : http://www.cms.gov/Medicare/Medic are-Fee-for-ServicePayment/sharedsavingsprogram/Do wnloads/2012-11-ACO-qualityscoring-supplement.pdf Slide Content Courtesy of Premier, Inc. 21
    • 22. Breaking Down Modes of Data Collection: The “CMS MSSP-33” 33 1 EHR meaningful use measure 3-claims only measures 22 hybrid measures 7 CG-CAHPS 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 Key assets required None Claims engine „GPRO-like‟ tool MSSP-33 engine Survey administration *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 Slide Content Courtesy of Premier, Inc. 22
    • 23. A MSSP GPRO Example: Breast Cancer Screening Data Collection 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. 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. Slide Content Courtesy of Premier, Inc. 23
    • 24. 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 Slide Content Courtesy of Premier, Inc. 24
    • 25. Strategies for the Future Supporting ACO measurement and performance 25
    • 26. 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 26
    • 27. 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. 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 Survey Results • Incorporate CG CAHPS results from CMS or the ACO‟s survey vendor by population or provider 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 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 27
    • 28. 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 • Solutions of the Future Issues managing the project during submission • • • • Strong process for supporting required clinical data collection and submissions • • 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-todate instructions from CMS specifications; integrating rule sets across multiple sources • 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 GPRO auto-filled fields are uploaded into QI “one button” GPRO XML file generation • 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 Data collection progress reporting, based on years of HEDIS experience • Ample licenses for access to QI • Limited access at one time - 10 users - hard to manage if all licenses went to reviewers • • GPRO technical performance is weak Year-round access to data to monitor performance and benchmarking 28
    • 29. Provider Chase Logic Configuration By carefully building provider chase logic designed for each quality measure, the manual data collection process is streamlined and focused. GPRO Module Selected Chase Logic (Short Desc) Verisk Health Default Customer Chase # Chase # Max Max by Max by Chase Provider Selected Chase Logic (Long Description) ACO 12: Medication Reconciliation CARE 1 3 PCP most m-year Card recent visit Endo most m-year PCP assign at end of m-year Fac of another admit m-year CARE 2 53 4 7 43 11 53 69 34 11 53 69 34 11 53 2 34 60 53 2 34 60 ACO 13: Falls: Screening for Future Fall Risk PCP most m-year Provider most m-year OB/G most m-year Fac of another admit m-year PREV 53 4 7 43 11 1 1 1 1 1 PCP with the most visits in the measurement year Cardiologist with most recent visit Endocrinologist with the most visits in the measurement year PCP assigned as of the end of the measurement year Facility of another admission in measurement year 1 1 1 1 1 1 1 1 PCP with the most visits in the measurement year Provider with most visit during measurement year OB/GYN with the most visits in the measurement year Facility of another admission in measurement year 1 1 1 1 1 1 1 1 PCP with the most visits in the measurement year Cardiologist with the most visits in the measurement year OB/GYN with the most visits in the measurement year PCP with the most visits containing immunization codes 3 ACO14: Preventive Care and Screening: Influenza Immunization PCP most m-year Card most m-year OB/G most m-year PCP most w/ imm codes 1 1 1 1 1 3 29
    • 30. Integrated Data Abstraction – MSSP Diabetes 30
    • 31. 3. Compliance with CMS ACO Quality Measures 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 Process June – 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 31
    • 32. Measure Rate Summary – Tobacco Use ACO 17 (GRPO PREV-10) (NQF #0028): Preventative Care and Screening: Tobacco Use: Screening and Cessation Intervention Population: Adjudicated Claims and Electronic Clinical Data Data Elements Full Beneficiary Population General Measure Data 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 Measurement Year: 01/01/2013 - 12/31/2013 52.86% Refresh Date: 09/18/2013 Description: Sample: Electronic and Abstracted Clinical Data (for CMS Submission) Data Elements CMS Total Sample Including Oversample Denominator Exclusions Final Denominator for Analysis Patients Compliant through Clinical Data Only General Measure Data 616 35 411 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. 265 Reported rate 64.48% Lower 95% confidence interval 59.73% Upper 95% confidence interval 69.23% Sample: Adjudicated Claims and Clinical Data (Electronic and Abstracted) Data Elements Final Denominator for Analysis Denominator Exclusions Patients Compliant through Claims Data Only Patients Compliant through Clinical Data Only General Measure Data 411 35 112 265 Reported Rate 91.73% Lower 95% confidence interval 86.18% Upper 95% confidence interval 97.28% Three different rates are produced for each MSSP GPRO measure: segregating data and then combining data sources for different purposes (reporting vs. monitoring). 32
    • 33. 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 33
    • 34. Available Benchmarking 34
    • 35. 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 35
    • 36. 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: QRM # 3063(E) Model Type Condition Description Gap Diabetes (E) Patients without HbA1c test in the last 12 months. Patients taking insulin and sulfonylureas at the same time. Patients without home glucose measurement devices in the last 12 months. Patients without diabetes-related office visit in the last 12 months. 8574(E) Gap Diabetes Diabetes taking insulin in the last 12 Gap months (E) Diabetes-related admission in the last Gap 12 months (E) Diabetes-related ER visit in the last 12 Gap months (E) 3401(E) Gap Diabetes + Hypertension + Obesity (E) Patients without antihyperlipidemic drugs in the last 12 months. 8855 8836(E) 8661(E) Patients without office visit in the last 12 months. 3401 Gap Diabetes + Hypertension + Obesity Patients without antihyperlipidemic drugs in the analysis period. 3147 Risk Diabetes Patients with complicated lipid disorders. 3140 Risk Diabetes Patients with hyperlipidemia. 36
    • 37. 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 37
    • 38. 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 38
    • 39. Mirroring CMS Util. / Cost Report for Drill Down Medicare Shared Savings Program Aggregate Expenditure/Utilization Trend Report ACO A1166 <ACO Name> Year 2013, Quarter 1 ACO-Specific Assigned Beneficiaries1 Annual ACO Cohort1 Annual Total Expenditures per Assigned Beneficiary Medicare Enrollment Type2, 4 Total End Stage Renal Disease Disabled Aged/Dual Aged/Non-Dual 9,402 60,180 9,141 14,999 9,086 8,979 61,192 8,242 11,986 8,263 Component Expenditures per Assigned Beneficiary3 Inpatient4 Indirect Medical Education (IME)4 Disproportionate Share Hospital (DSH)4 Skilled Nursing Facility Institutional (Hospital) Outpatient Part B Physician/Supplier Home Health Durable Medical Equipment 3,342 38 188 578 2,507 2,141 352 315 2,959 121 295 663 1,456 3,013 490 285 323 Functionality that 1) will allow drill down by provider and patient for root cause action, and 2) will allow validation of CMS reports 214 Hospice 39
    • 40. Key Take Home Messages 1. You are not alone – there are common transformation objectives  2. There are a myriad of factors that contribute to risk management success  3. Understand the complexity of contractually required quality measurement Planning and executing relevant functionality is a critical success factor  6. Seek external assistance to turn data into information as data is a key foundation Meaningful measures are key to evaluating accountability  5. This transformation requires new ways of collecting, managing and analyzing data Taking risk for populations means effectively managing their health  4. Many providers are going down the path of taking on population risk management Have effective informatics and electronic quality measurement strategies Verisk Health is delivering integrated provider products to meet key needs  The focus is supporting ACO measurement and performance improvement 40

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