Converting BIG Data into Value. Alan Krumholz MD, FAAP, DFACMQ

Transcription

1 Converting BIG Data into Value Alan Krumholz MD, FAAP, DFACMQ

2 Disclosure Statement I have no financial COI issues to disclose Neither myself nor Mayo Clinic endorse any of the sponsors of this meeting

3 Mayo Clinic Health System MCHS employs over 900 providers in Iowa, Minnesota and Wisconsin.

4 One System Four Regions Moving from volume to value, but different approaches to contracting (commercial ACOs, employer contracts, no contracts) Focused on proactive patient management, but varied priorities and resourcing (PCMH, disease-specific outreach, etc.) Previously limited view of population and disparate access to claims data, but all looking for more sophisticated clinical analytics

5 Alice s Paradox If you don t know where you are going any road will get you there! - Lewis Carroll, Through the Looking Glass Corollary for Healthcare: To know how to improve we must measure it!

6 Humedica MinedShare Implemented in October 2012 to bring together clinical and cost data Governance and delivery focused on: 1. Education Weekly region-specific training sessions to analyze and discuss data trends 2. Adoption Formal request/review process that asks: What are you going to DO with the data?

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8 Adding the Clinical Dimension Patients missing BMI screening DM patients missing A1c test Coded HF patients Patients w/ BMI > 35 DM patients w/ A1c > 9 DM patients in control on A1c, LDL and BP Patients w/ EF < 40 but no HF code HF patients not on ACE/ARB HF patients at-risk for IP stay

9 Examples of Humedica MinedShare Reports in Use Preventive Services (E&Ms, mammograms, colonoscopies, BMI screenings, etc.) High Utilizers (ED frequent fliers, readmits, patients missing PCP follow-up visits, etc.) Chronic Disease Management (Diabetes, Hypertension and Heart Failure screenings, risk stratification and clinical outcomes, etc.) Panel Management (risk adjusted panel sizing, RVUs, control rates, E&M utilization, etc.)

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22 Additional Humedica Opportunities Uncoded chronic disease patients CHF patients missing EF reading Patients with > 5 ED visits (12 months) Mean RVUs by Risk Score (by PCP) COPD, Diabetes at-risk for admissions (MinedShare predictive model)

23 Key Questions Prior to Release (Clinical Data) Is the data easy to understand-or is training required? Has the data been vetted? Are there potential inaccuracies in the data? What levels of access do you authorize to how many people? How much training and understanding is needed to be released as a super user Controlling read access vs. write access

24 What is WHIO? WHIO is the Wisconsin all payer database Incorporated in late 2005 Organization of Organizations Providers Payers Purchasers State of Wisconsin WHIO uses Ingenix as its vended datamart Ingenix uses symmetry s ETG grouper as it base

25 Key Questions Prior to Release (Claims Data) Is the data easy to understand-or is training required? Has the data been vetted? Are there potential inaccuracies in the data? Is vetting an option? Who has access to the database? How much understanding is needed prior to the release of the data? What are the limitations of a claims based reporting system?

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28 Cost Summary Breakdown by Site Site ER Hosp Svc Lab Pharmacy PCP Radiology Specialty Overall Cost Overall Quality A B C D E F G Competitor services driven Rad-MRI driven encounter driven both p<0.05

29 Clinic A Providers ER Hosp Svc Lab Pharmacy PCP Radiology Specialty Cost Quality Case Mix A B C D Services driven Encounter driven Both Cost Breakdown by Site Overall

30 So--- Is there a relationship between cost and quality? Copyright 2013 Mayo Foundation for Medical

31 Avg A1c Copyright 2013 Mayo Foundation for Medical Charges vs HbA1c Results y = x R 2 = Total Charges

32 Last A1c and Number of Encounters Last A1c [In Time Period] Linear (Last A1c [In Time Period]) 16 A1c y = x R² = # of Encounters

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35 The Value of Big Data From Large Collaborative Databases Understanding how you are performing compared to other similar organizations Accurate risk adjusting models Ends the our patients are sicker response Allows for normalization of local charge variations Allows for predictive modeling tools

36 Key Takeaways Learn your data before using it Examine: Find the trends in your population Diagnose: Focus on the actionable opportunities Treat: Design evidence-based interventions Choose opportunities that are sized to current resources Balance centralized standards with customized applications Design initiatives with measurement in mind

37 Key Takeaways Governance is critical Maintain control of data requests Require use plan before data mining Ensure end user understanding of data prior to release Validate that data provided is being used to improve processes and Measure outcomes-did results improve?

38 Questions? Copyright 2013 Mayo Foundation for Medical