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Belen Fraile MD, MSc

ANALYTICS & POPULATION HEALTH MANAGEMENT INFRASTRUCTURE AT A COMPREHENSIVE CANCER CENTER. Belen Fraile MD, MSc Vice President, Analytics & Population Health Management belen_fraile@dfci.harvard.edu. October 2 nd 2018. Population Health Management & Decision Support Program Update.

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Belen Fraile MD, MSc

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  1. ANALYTICS & POPULATION HEALTH MANAGEMENT INFRASTRUCTURE AT A COMPREHENSIVE CANCER CENTER. Belen Fraile MD, MSc Vice President, Analytics & Population Health Management belen_fraile@dfci.harvard.edu October 2nd 2018

  2. Population Health Management & Decision Support Program Update

  3. INTRO | Payment Reform in Oncology • Quick Cancer Facts • Health care costs for Oncology getting more and more press attention

  4. INTRO | Payment Reform in Oncology • Overview: • Most new payment and delivery models are being piloted in the private sector • Implications of CMMI Oncology Care Model (OCM) • Alternative Payment models: • Patient Centered Medical Home (PCMH) • Episode Based Payments/ Bundle Payments • Accountable Care Organization (ACO) & Shared Risk Model • Value-based Payments tied to Pharmaceuticals

  5. INTRO | PHM Mission & FY18 Goals • PHM vision is to assure DFCI thrives in a Value-based care (VBC) environment by: • Conducting advanced analytics to demonstrate our value in a measurable manner • Facilitating interventions (analytic support and impact evaluation) to improve quality and reduce costs. • Supporting system relationships & partnerships (ACOs, local payers and other stakeholders) • This FY18 the PHM Department Goals are focused in 4 areas: • 1- Oversee Pay-for-Performance program (Shared with Diane Lanahan & Joe Jacobson) • 2- Review Payer, ACOs, Employers Strategy (Shared with Diane Lanahan & Elizabeth Liebow) • a) Identify opportunities for alternative reimbursement models across all stakeholders • Population Heath Management interventions • Facilitating system relationships • b) Developing capacity to be held accountable (and potentially sharing risk) for the care that we provide through: • 3- Deploy STRATA as Financial Decision Support Tool at the Institute by providing the institution with: • A team of system super users and administrators capable to leverage the tool and train end-users • A robust cost accounting capacity • 4- Conduct advanced analytics to show the Value that our Institution brings to the oncology community (cost-efficiency and outcomes) by leveraging available large databases: • All Payers Claims Database/APCD • Medicare Database/ VRDC

  6. PHM FY17 Goals |Acute Care Modelling Understanding ED visits for Oncology patients and evaluating an urgent care setting: 2,000 actively managed patients triggered more than 5,000 ED visits (only at BWH) during FY14. 57% of the visits were oncology related • Volumes were estimated based on average volumes per day, per hour and per discharge disposition. • Per day, we expect to see an average of 6 patients; per hour, an average of 1.5 patients • In a weekday, such as Monday, we would expect to see an average of 8 patients. The chance of seeing more than 10 patients is less than 25% and less than 3 patients is less than 5%. • 72% of the patients arrive during clinic hours and Fridays and Mondays are the highest volume days. • 60% of the patients are hospitalized after the ED visit The Table represents the results of the regression model to explain (r-squared=0.65) how the different service combinations are provided to patients at the ED and how each of the services marginally contributes to the overall LOS. 6

  7. PHM FY17 Goals |Intervention Impact Clinical Pathways impact: Preliminary data that compares pre & post-implementation data of Clinical Pathways in the Thoracic group shows an improvement in survival and a reduction in costs. • The unadjusted 12 month cost for the Post Pathway cohort is $14,119 (22.5%) less than the Pre Pathway cohort, and the difference is statistically significant (p=0.03). • The adjusted cost for the Post Pathway cohort is $15,237 (24.4%) less than the Pre Pathway cohort, and is still significant (p=0.01). The adjustments include age (>65), gender, race, distance to the DFCI, Clinical trial enrollment and Positive genetic profile. • The average utilization of services is less across most service areas and statistically significant in chemotherapy, non-chemotherapy infusions, other diagnostic tests and procedures. • The 12 Month Kaplan-Meier Survival Curves show the post pathway cohort has better survival rates than the pre cohort • However, the log-rank and Wilcoxon signed-rank test is not significant, which means the difference between two groups is not statistically significant (p=0.13). • The unadjusted Hazard ratio for the post pathway cohort is .75 (P-value=0.07), which shows the risk of death is 25% lower for the post pathway cohort, but it is not statistically significant. • The adjusted Hazard ratio for post pathway cohort is .67 (P-value=0.015), which shows the risk of death is 33% lower for the post pathway cohort and it is statistically significant. The adjustments include age (>65), gender, race, distance to the DFCI, clinical trial enrollment and Positive genetic profile.

  8. PHM FY17 Goals |End-of-Life Utilization End of Life of oncology patients: End of Life quality metrics have remained stable with no change over the past few years. • NQF standard metrics were adapted to fit commercial claims to understand EOL in this population

  9. PHM FY17 Goals |Value • DFCI and the 11 ADCC: the highest survival rates for cancer1 and comparable costs2 to other oncology providers • Medicare datashows that at DFCI the likelihood of a patient surviving their cancer after five years is the highest compared to other NCI comprehensive cancer centers, Academic Medical Centers and Community Settings. • At the same time, the costs of ADCC attributed patients are comparable over time to other providers • Using Medicare claims data from 2006-2011, the study identified patients newly treated for cancer, continuously enrolled for 5 years and assigned to a single provider (found to have the greatest total Medicare reimbursement using claims within the first 180 days of treatment for cancer) • The analysis includes 800,000 cancer patients nationally, and contains all Medicare claims including inpatient, outpatient, physician, skilled nursing, hospice, DME, and home care. NOTES: 1- Pfister, David G., et al. "Risk adjusting survival outcomes in hospitals that treat patients with cancer without information on cancer stage." JAMA oncology 1.9 (2015): 1303-1310. 2- Analysis conducted by the Alliance of Dedicated Cancer Centers (ADCC)

  10. Clinical Operations & Business Analytics (COBA) • Program Update

  11. DFCI COBA | 1- COBA MISSION MISSION Enable analytics across the Institutethrough the provision of consistent, reliable information through robust data sources and user friendly reporting applications FOUNDATIONAL PRINCIPLES • Centralized Common Analytic Capacity that offers a shared data warehouse for both, the Research and Clinical, Operational enterprises and core set of metrics and definitions that the different teams and individuals will share and leverage for their business or clinical practice needs • Founded over Stewardship Model in which the different areas will have their own analytic advocates driving the analytic agenda and development at the institute ROADMAP • FOUNDATIONAL- Redesign Clinical Operations & Business Analytics services to existing business units users of information- Core Analytics • TRANSITIONAL- Expand analytic services to other business units & Clinical Departments • TRANSFORMATIONAL- Expand analytic capacity within all existing business & clinical units – Advanced Analytics TRANSITIONAL TRANSFORMATIONAL FOUNDATIONAL 12- 24 months <12 months >24 months Basic Descriptive Analytics Advanced Predictive Analytics

  12. DFCI COBA | 1- COBA MISSION Hosting, collecting & processing data so it is available for use – Harmonized DW, Data Marts, Data Cubes Data Management Production of Reports and Descriptive Analytics- Operational Dashboards Reporting Delivery of analysis that examine and evaluate data to draw conclusions/ turn into actions- Trend analysis, Performance Dashboard Analytics Facilitating analysis to allow the stakeholder answer complex questions – Modelling, Simulations, Sensitivity analysis Advance Analytics 10M FOUNDATIONAL 1- Operating Model Redesign (0-18M) 2- Governance & Stewardship Model Create Analytics Community of Practice 3- Building Capability • Skill/Competency Model • Data Warehouse • Data Dictionary • Analytic Tool assessment & Implementation ANALYTICS ENABLEMENT

  13. DFCI COBA | Unified Analytics Platform • Single Platform that serves all business, operational, clinical and • research units at the Institute • Single source • Shared metrics & definitions • Shared platform • Access & Stewardship foundation • Shared single team Marts (SAMs) Sources

  14. DFCI COBA | Unified Analytics Platform ANALYTICS PLATFORM

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