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Outcomes-based Contracts between Pharma and Payers Preparing for the Future, Now!

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This presentation throws light on:
• What is outcomes-, risk-, or value-based contracting?
• What is driving this?
• How does it benefit pharma, payers, patients?
• What metrics are critical to develop then monitor these contracts?
• How can pharma prove the safety, efficacy, and cost effectiveness of its products, demonstrating the impact and outcomes needed to meet contractual obligations?

For more information on our Life Sciences solutions, please
http://www.sciohealthanalytics.com/offerings/solutions/commercial-excellence

Published in: Healthcare
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Outcomes-based Contracts between Pharma and Payers Preparing for the Future, Now!

  1. 1. |1©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved.©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. SCIO HEALTH ANALYTICS® Outcomes-based Contracts between Pharma and Payers: Preparing for the Future September 28, 2017
  2. 2. |2©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. OUTCOMES-BASED CONTRACTING: WHERE WE ARE TODAY
  3. 3. |3©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. DEFINITIONS AND DRIVERS The US healthcare system, facing ever increasing care costs, need to improve the efficient and appropriate use of healthcare products and services. • Drug prices are a concern, impacting patient care and compliance. • Several payors have informed us that for the first time drug benefit costs have surpassed medical benefit costs. Outcomes-based contracting is intended to align the medicine or device price to its benefit. Manufacturers need to demonstrate the value of their products in terms of clinical efficacy, as well as economic and quality-of-life outcomes via costs. Common arrangements tie rebates (or discounts) to a measurable patient outcome in the given disease area. • First, each organization must align on what “risk” and “outcomes” mean to them. By creating an advanced model of the concurrent and prospective patient universe a harmonized agreement on which patients are risk stratified is agreed upon. • The manufacturer agrees to provide a rebate to a payer if the observed performance of a specific medicine doesn’t reach the agreed-upon threshold and therefore the care cost of the patient increases. • If the product performs as observed in clinical trials (or better), the payer does not receive a rebate. In turn, the partnership agree to message to additional patients who are likely to change their behavior and be most receptive to comply with the treatment regimen.
  4. 4. |4©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. BENEFITS Manufacturers Payers Patients • Ability to unbiasedly prove value of product(s) based on patient outcomes in the real world • Market expansion based on ability to treat a larger, risk stratified patient population • Stronger partnership with payor leading to future “win/win” arrangements • Truly being “patient centric” • Ability to share outcomes messages to their customers (HCPs, Institutions, IDNs, etc.) which drives focused messaging and ROI • Measurable lower costs by risk stratified patient type • Recognized value for the price being paid • Sharing in risk with Manufacturer • Lower out-of-pocket annual healthcare expenses • Better outcomes: clinical, economic, and quality-of-life • Focus on preventative care versus reactive care • Being part of a true “for me” partnership between Life Sciences company and Payers • Gamification…other incentives to make it “real” to the patient A Win-Win- Win
  5. 5. |5©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. KEY SUCCESS FACTORS • Both manufacturers and payers need to commit the time and resources for the design, implementation, and monitoring of outcomes-based contracts. • Having a 3rd party experienced analytics team create the patient model removes any doubt of each side being “self fulfilling”. Commitment and Trust • Need to define the outcomes or “endpoints” that will be monitored over time and to agree on an “acceptable” levels of success. Clearly Defined Metrics • Let’s face it, not all conditions and medications/devices are good candidates for outcomes based contracting. • The selected conditions and products need to have endpoints that can be easily obtained through the patient model plus reliable and objectively agreed upon while being quantitatively, not subjectively, measured in a fair timeframe. Selection of Conditions and Products
  6. 6. |6©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. OUTCOMES-BASED CONTRACTING: UNDERSTANDING PATIENT PERSONAS
  7. 7. |7©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. PREDICTIVE ANALYTICS CREATES AN ENHANCED VIEW OF THE PATIENT FOR ENGAGEMENT DATA Claims Data TRXEligibility Socio- economic Lab EMRFitness Devices • Medical and Pharmacy Claims identify prospective high risk patients • Understand how chronic conditions play a role in adherance/compliance • Identify Personas with no claims data but social economic and eligibility data based on cohort analysis DATA PRODUCES PREDICTIVE ANALYTICS Prospective risk models • Low risk • Moderate risk • High risk Impactablity risk for clinical care gaps • Low – Low – Moderate – High Prospective Risk Undiagnosed Population • Moderate – Low – Moderate – High • High – Low – Moderate – High Identify pain points - Adherence/Compliance, increasing cost of care Identify messages and interventions by Persona type Use of SCIO® risk models to predict • Avoidable hospital events • readmissions • End of Life Add clinical risk metrics i.e. lab or high tech imaging • Adjust risk for clinical plus financials Pool risk in finer pools or quintiles of risk- IDN’s • Low risk- monitor and track risk migration and steer towards high quality providers • Moderate risk- shift to more Accountable Care Mgt Functions • High Risk – shift towards capitated structures Medical Reduction – Quality/ Cost Treatment Pathways/lower cost
  8. 8. |8©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. OBJECTIVE OF PATIENT PERSONA’S PROVIDER Integrate Multiple Data Sources : Additional de-identified medical & pharmacy claims data, EHR data, ZIP level demographics, prescriber level data, patient registry data, segmentation data and outreach data can add to, and further deepen the insights of the prediction of treatment pathways. PREDICTIVE ANALYTICS PRESCRIPTIVE ANALYTICS Dynamic Risk Management of Patient Populations based on Outcomes and goals 1 Treatment Pathways individualized for each Persona driving Outcomes 3 Patient Migration Towards greater Compliance 4 Provider Performance Management measuring Outcomes 2 PATIENT
  9. 9. |9©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. BALANCED ADULTS 12% 68% 19% 1% 65+ 55-64 35-54 16-34 Age Group 27% 43% 22% 8% $200k+ $100k - $200k $50k - $100k Less than $50k Estimated Income Description Well managed and balanced middle age adults. Mostly female, predominately with college level education and white collar employees with medium income. They are healthy population with high utilization or ER services and PMPM cost which is aligned to average number of chronic conditions. Intervention: High risk and highly impactable patient population which require greater degree of social intervention plus specialist intervention. Demographic Attributes % Above Poverty Level 94% % Blue Collar Employed 12% % Single Family Dwelling 73% % Household with children 62% Clinical Attributes IP Utilization 1.72 ER Utilization 1.34 # Average Chronic Conditions 1.22 Paid Amount PMPM ER $72 Paid Amount PMPM IP $42 Gender Education High School College Individuals with Income Level > $50K Median Age 42 $385K 73% Frequent Spending Median Home Value Socio-Economic Score 90 70 100 Spending Pattern High Risk Prospective Risk Score 86 Impactability Score 78
  10. 10. |10©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. OUTCOMES-BASED CONTRACTING: CASE STUDY: RISK STRATIFYING OUR PATIENTS WITHIN CONGESTIVE HEART FAILURE
  11. 11. |11©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. CONGESTIVE HEART FAILURE – PATIENT ANALYTICS STUDY PATIENT SAMPLE POPULATION • CHF patients: 4,281 • High Risk CHF: 3,640 • Medium Risk CHF: 641 • One or more Comorbid conditions (COPD, Diabetes, CAD): 2,726 • ARB or ACE usage: 2,347 • Product A usage: 566 • Product B usage: 238 3,640 4,281641 2,7262,347566238 5.5% 13.2% 14.97% 54.82% 63.67% 85.02%
  12. 12. |12©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. CONGESTIVE HEART FAILURE – PATIENT ANALYTICS STUDY • Facility PMPM accounts for 71% of Total PMPM • High risk patients are the top 15% sickest patients • High risk patients are 10x more costly than Medium risk patients
  13. 13. |13©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. CONGESTIVE HEART FAILURE – PATIENT ANALYTICS STUDY • High and Medium risk Patients on Product A have significantly lower costs of care than patients not on Product A • Particularly for Facility PMPM costs for High Risk patients
  14. 14. |14©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. CONGESTIVE HEART FAILURE – PATIENT ANALYTICS STUDY • Patients on Product A therapy have 39% lower PMPM Total costs compared to patients not on Product A therapy and 21% lower PMPM Total costs compared to patients on ACE or ARB therapy
  15. 15. |15©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. CONGESTIVE HEART FAILURE – PATIENT ANALYTICS STUDY • Comparing Patients on Product A to baseline (all CHF Patients), Patients not on Product A therapy, and patients with 1+ Comorbid conditions • Product A, Non Product A and 1+ Comorbidity Patient segment values are % deviations from baseline. ALL CHF Patients Product A Patients Non Product A Patients 1+ Comorbidity* Patients High Risk Medium Risk High Risk Medium Risk High Risk Medium Risk High Risk Medium Risk Inpatient acute 1.2 <1.0 -31.3% -100.0% 0.5% 1.9% 6.7% 11.5% Emergency room 1.0 <1.0 -28.9% -31.6% 0.5% 0.8% 8.5% -1.6% Evaluation & management 29.9 5.8 -26.1% -0.6% 0.4% 0.0% 6.8% 10.4% Outpatient medical 28.4 6.2 -19.6% 58.0% 0.3% -1.3% 6.0% 10.7% Outpatient surgical <1.0 <1.0 -22.3% -100.0% 0.3% 2.7% 8.4% 21.6% Ancillary 34.5 7.1 -27.2% 9.8% 0.4% -0.2% 5.8% 14.5% Patient Utilization By Segment per year
  16. 16. |16©2017 SCIOInspire, Corp. d/b/a SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. CONGESTIVE HEART FAILURE – PATIENT ANALYTICS STUDY • Takeaways: • Patient costs, across the board, for our drug (Product A) are significantly lower than the baseline (All CHF) and for our competitors (Non Product A Patients) • Need to focus the message on the lower total care costs and share how interventions can be driven to each Patient Persona ALL CHF Patients Product A Patients Non Product A Patients 1+ Comorbidity* Patients High Risk Medium Risk High Risk Medium Risk High Risk Medium Risk High Risk Medium Risk Inpatient acute $31,022 $812 $15,671 $0 $31,259 $830 $32,069 $600 Emergency room $2,236 $549 $1,088 $113 $2,254 $559 $2,538 $604 Evaluation & management $4,622 $762 $3,283 $784 $4,643 $761 $4,651 $811 Outpatient medical $20,321 $1,576 $14,874 $1,802 $20,405 $1,571 $20,582 $1,970 Outpatient surgical $4,615 $637 $4,269 $0 $4,620 $651 $4,976 $762 Ancillary $13,197 $1,445 $9,194 $1,312 $13,259 $1,448 $13,253 $929 Patient Utilization By Segment per year

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