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RowdMap at DATALAB at Health Datapalooza 2015

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HHS has so much data! Medicare, substance abuse and mental health, social services and disease prevention are only some of the MANY topical domains where HHS provides huge amounts of free data for public consumption. It’s all there on HealthData.gov! Don’t know how the data might be useful for you? In the DataLab you’ll meet the people who collect and curate this trove of data assets as they serve up their data for your use. But if you still want inspiration, many of the data owners will co-present with creative, insightful, innovative users of their data to truly demonstrate its alternative value for positive disruptions in health, health care, and social services.

Moderator: Damon Davis, U.S. Department of Health & Human Services

Panelists: Natasha Alexeeva, Caretalia; Christina Bethell, PhD, MBA, MPH, Johns Hopkins; Lily Chen, PhD, National Center for Health Statistics; Steve Cohen, Agency for Healthcare Research & Quality; Manuel Figallo, Sas; Reem Ghandour, DrPH, MPA, Maternal and Child Health Bureau; Jennifer King, U.S. Department of Health & Human Services; Jennie Larkin, PhD, National Institutes of Health; Brooklyn Lupari, Substance Abuse & Mental Health Services Administration; Rick Moser, PhD, National Cancer Institute; David Portnoy, MBA, U.S. Department of Health & Human Services; Chris Powers, PharmD, Centers for Medicare and Medicaid Services; Elizabeth Young, RowdMap

Published in: Data & Analytics, Healthcare
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RowdMap at DATALAB at Health Datapalooza 2015

  1. 1. PhDs can use open health data But the goal is to open it to the masses and let 1000 flowers bloom. Inother words, can these guysuse it? Let’s give it a shot Working with open health data at RowdMap, Inc. for about a year
  2. 2. Government is releasing lots of data…. And it’s been hard work…. But now you don’t need a PhD to use this data in a meaningful way … For mechanics of how to do this: http://goo.gl/Y64Fa2 Have an Idea? Attend Bootcamp: HealthCare Entrepreneurs’ BootCamp Tomorrow , 4:15pm Lincoln 2-3-4
  3. 3. So… there’s a lot of data and talk out there
  4. 4. Government performance data Government provider etc. data Government socio-demo data Consumer web / social data Analysis-based derived data Sentiment as a Key Driver (psychographic) - measured by Index scores for: - Domains (chronic, wellness, quality of care, customer satisfaction, customer service); - Brands (parent org and you individually) Market Growth; Census; Healthy Food; County Health Rankings & Indicators; Behavioral Health Factors; etc.* Dartmouth Atlas; STAR; Hospital Compare; Actual, Expected & Predicted Readmissions; Part B & D, etc.* STAR; Price, Bid, Rebate; Hospitals, Nursing Homes; Market, etc.* * Dozens of Primary Data Sets, updated at various frequencies When we say a lot…we mean a lot.
  5. 5. Let’s cut through the buzz
  6. 6. And it’s powerful, disruptive, game changing David Wennberg, RowdMap Advisory Board
  7. 7. New Government Released Referral Data (Patient flows between PCPS, specialists, hospitals and post acute centers) Dartmouth Atlas for Unwarranted Variation (Decades of research and data on unwarranted variation by condition and geography to keep things apples-to-apples for comparisons, hence “Unwarranted” in the name) New Government Released Performance Data (Individual providers, groups, hospitals and post acute centers including the new part B&D) Provider Pattern Intensity Profiles and Risk Readiness for every provider, hospital, post acute center in the US. All preloaded with no IT. OPEN DATA – Particularly powerful when pulled together Affordable Care Act data to determine Risk-Readiness of Providers / Networks
  8. 8. CMS: 50% of FFS will be gone by 2018 The business context has changed- health plans, government payers, providers, and hospital systems need to develop Risk-Readiness SM strategies to excel as they transition from fee-for- service to pay-for value.
  9. 9. Featured Nationally US CTO on RowdMap: “Visionary Genius”
  10. 10. What you can do [without a PhD] With mashups of gov’t data (CMS HHS, Gov, CDC) Chronic prevalence & physician supply Population Health Report Population Report Card Match practice patterns to the right risk arrangements – PFV Readiness Group Risk-Readiness SM Report Physician Risk-Readiness SM Report Hospital Risk-Readiness SM Report Post Acute Center Risk-Readiness SM Report Risk-Readiness SM Arrangement Match-Maker Manage clinical care and costs – Remove No Value Care Group Unnecessary Cost Report Physician Unnecessary Cost Report Hospital Unnecessary Cost Report Post Acute Center Unnecessary Cost Report Unnecessary Cost Referral and Value Chain Report
  11. 11. What you can do [without a PhD] With mashups of gov’t data (CMS HHS, Gov, CDC) Chronic Prevalence & Physician Supply Match Practice Patterns to the right Risk Arrangements – PFV Readiness Manage Clinical Care and Costs – Remove No Value Care
  12. 12. Diabetes Prevalence - Westchester Use this data to allocate providers and care management resources around condition-specific population needs by zip. Locate clinics, health fairs, etc. based on chronic needs. Income Obesity Depression Health Opportunity Index Demand and Supply Lots of diabetics but few PCPs Lots of diabetics and lots of PCPs What type of populations? Medicare FFS Geo. Variation: http://go.cms.gov/1D8j7LE CDC Behavioral Risk Factor Surveillance: http://1.usa.gov/1PzcisT Medicare FFS Part B: http://go.cms.gov/OCmyoy Medicare FFS Part D: http://bit.ly/1mGyBxk PCP Density – Westchester
  13. 13. 15 Demand and Supply County Profiles Largest Counties In Ohio People use this data to calibrate expectations for profitability by incorporating population health and provider performance into product strategy. Use excess to subsidize operations in counties with fewer high-performing resources Risk Scores Total Cost PMPM Reimbursement Overall Star Chronic Star Health Rank MA Profit Opportunity - MA Profit Opportunity - Exchange MA Eligibles MA Enrolled Exchange Subsidize d Exchange Enrolled Compare to National and Regional Benchmarks Medicare FFS Geo. Variation: http://go.cms.gov/1D8j7LE CDC Behavioral Risk Factor Surveillance: http://1.usa.gov/1PzcisT Medicare FFS Part B: http://go.cms.gov/OCmyoy Medicare FFS Part D: http://bit.ly/1mGyBxk
  14. 14. What you can do [without a PhD] With mashups of gov’t data (CMS HHS, Gov, CDC) Chronic Prevalence & Physician Supply Match Practice Patterns to the right Risk Arrangements – PFV Readiness Manage Clinical Care and Costs – Remove No Value Care
  15. 15. At the core of Risk-Readiness SM is Unwarranted Variation: RowdMap applies the Dartmouth Atlas for Unwarranted Variation methodologies to data on Medicare Parts B & D. This research has been repeatedly validated over the last 30 years and we now have a national data set to apply the methodologies at a large scale. The estimated 30% of medical expense that goes to unnecessary care. This unnecessary spend drives billing in a fee-for-serve economic model, but success in pay-for-value comes from managing and mitigating these pockets of variation. Every provider has a unique practice pattern that informs Risk- Readiness SM Pay for Value Readiness
  16. 16. Los Angeles, CA Compare to National or Regional Benchmarks Pay for Value Readiness Provider Profiles Identify highly efficient, Risk-Ready practices and physicians to profitably grow into. Improve profitability of lower performing practices with large panel sizes through modified arrangements or performance improvement plans. Medicare FFS Part B: http://go.cms.gov/OCmyoy Medicare FFS Part D: http://bit.ly/1mGyBxk Referrals: http://1.usa.gov/1FzoEOV
  17. 17. Identify high and low performing hospitals and post-acute facilities— are there post acute facilities that hospitals with poor chronic readmits are routing members to? Pay for Value Readiness EOL Hosp Days: Which hospitals fewer end-of-life days than their peers? Chronic Admits: Which hospitals see their most chronic population repeatedly/ with the most frequency? Cardiac Imaging: Which hospitals are more likely to over-utilize cardiac imaging compared to their peers? Dartmouth Atlas: http://bit.ly/1GXvlJp CMS Hospital Compare: https://goo.gl/p8MtoI CMS Hospital Readmissions: http://goo.gl/02KnQd CMS Nursing Home Compare: https://goo.gl/3DpT8m
  18. 18. Pay for Value Readiness Great profile for aggressive risk Tread carefully for some risk Match appropriate risk arrangements based on provider practice patterns and Population characteristics within a geography.
  19. 19. What you can do [without a PhD] With mashups of gov’t data (CMS HHS, Gov, CDC) Chronic Prevalence & Physician Supply Match Practice Patterns to the right Risk Arrangements – PFV Readiness Manage Clinical Care and Costs – Remove No Value Care
  20. 20. Remove no-value Care Manage Unnecessary Spend Risk-Readiness℠ looks at a different category of spending Shift focus from clinical edits, audits, and recovery efforts to identifying care that is clinically appropriate, but unnecessary. Historical efforts have shown returns, but they only look at a fraction of total spending. Unnecessary care can account for up to 30% of total spending and provides significantly larger opportunities for cost containment and quality improvement. Clinically Appropriate, but Unnecessary Care (30% of spend) Claims Spend for a Health Plan Necessary Utilization (70%) “It’s generally agreed that about 30 percent of what we spend on health care is unnecessary. If we eliminate the unneeded care, there are more than enough resources in our system to cover everybody.” -Dr. Elliott Fisher, Dartmouth Institute for Health Policy
  21. 21. Remove no-value Care Manage Unnecessary Spend RowdMap tackles the 30% of the U.S. health care spend that goes to clinically appropriate, but unnecessary care Over $9B in Orange County, CA How much unnecessary spend is in your market? Over $66B in Florida $850 Billion Unnecessary Spend* in 2014 Least Unnecessary Spend Most Unnecessary Spend RowdMap tackles the 30% of U.S. health care spend that goes to clinically appropriate, but unnecessary care. RowdMap’s models identify the cost-savings opportunities in a geography based on the collective intensity of care delivered by doctors in that area. * Unnecessary Spend = (Dartmouth Avg cost) * (Population) * (RowdMap Network Opportunity Index)
  22. 22. Remove no-value Care Manage Unnecessary Spend Unnecessary Spend in Florida In Broward Co. alone, there is over $7.6B in unnecessary spend. Let’s look at which hospitals, groups and physicians account for this and for what conditions
  23. 23. Physician Marketshare by Major Clinical Categories Remove no-value Care Manage Unnecessary Spend Match appropriate risk arrangements based on provider practice patterns and Population characteristics within a geography. Hospital Marketshare by Major Clinical Categories Provider Group Marketshare by Major Clinical Categories Unnecessary Spend in Broward By condition across hospitals, groups and physicians This Physician. Let’s start here This GroupThis Hospital Circulatory Muscular- skeletal Respiratory
  24. 24. Remove no-value Care Manage Unnecessary Spend All contents are proprietary to RowdMap, Inc. and are being provided on a confidential basis. Any use, reproduction or distribution of this information, in whole or in part, or the disclosure of any of its contents without the prior written consent of the Company, is prohibited. Physicians Driving Unnecessary Care in Broward Musculoskeletal care is major contributor to unnecessary spend in Broward. Let’s take a physician who is not an outlier but in the middle of the pack such as Dr. Spend*. Let’s walk through what his clinically acceptable, but medically unnecessary, practice pattern creates in unnecessary spend.
  25. 25. Remove no-value Care Manage Unnecessary Spend Referral Patterns and Physician Value Chains Identify high performing providers and downstream referral patterns. Encourage referrals to high-performing specialists.
  26. 26. Remove no-value Care Manage Unnecessary Spend Least Unnecessary Spend Most Unnecessary Spend Option 2: Reinforce highest-performing referral and care pathways. Increase the number of patient interactions with green dot doctors. Option 1: Change provider behavior. Requires lots of provider education. Requires payer to make up a significant portion of a provider’s revenue. Increase the number of green dot doctors. Zoom to zip
  27. 27. Remove no-value Care Manage Unnecessary Spend If had same ratio as : • His decompression rate would drop from 6.01 to 0.436 per patient. • Which translates to 2,608 fewer decompressions per year. • At an average cost of $332 per decompression, this represents potential savings of over $850K If decompression to fusion rate were average for orthopedic surgeons: • He would have 1629 fewer decompressions for a potential savings of $540K. *Actual physician names have been changed. For every 10 back fusions, does 103 decompressions For every 10 back fusions, does 2 decompressions. Dr. Save* Dr. Spend’s Dr. Spend* Dr. Save* That’s one physician, with one procedure, in one clinical condition. This savings would not be picked up in unit cost or utilization analysis, but cumulatively dwarfs fraud, waste and abuse outliers. Intense practice patterns like this power FFS arrangements but success in Pay for Value comes from identifying Risk-Ready providers. Dr. Spend*
  28. 28. Start with Data for Business Context then add Tech. The ACA at your finger tips For Payers & Providers

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