Minnesota Accountable Health Model: Data Analytics Subgroup MONDAY, DECEMBER 8, 2014

Transcription

1 Minnesota Accountable Health Model: Data Analytics Subgroup MONDAY, DECEMBER 8, 2014

2 Agenda Welcome and Overview of Agenda Review of the November Subgroup Meeting and MN SIM Task Force conversations Discussion of Shared Definitions and Basic Assumptions Discussion of Data Analytic Elements and Data Sources Brainstorm Guiding Principles to Motivate Alignment Next Steps

3 Re-cap of the 1 st Subgroup Meeting Monday, November 17 Presentations from ICSI, MNCM, Stratis and SHADAC Continue to build upon great work that has already been accomplished in Minnesota do not duplicate efforts Key themes from Subgroup members: Clear boundaries needed between Phase One and Phase Two work What is the audience for the Subgroup s products? Keep scalability in mind (small and large providers, urban and rural) Looking to the State and Task Forces to address areas where there may be differing views about the best approach

4 Feedback: Community Advisory Task Force Overall, pleased with the progress made thus far Interested in the connection between goals, questions to answer, potential value, and data analytic elements Keep the idea of who is going to use this clear throughout the work Suggest a core set of data analytic elements that ought to be standardized, so that if an individual changes plans five times, that standard information provides a consistent picture

5 Feedback: Community Advisory Task Force (continued) Reducing health care costs requires addressing social needs too, so address this as appropriate in Phase One, so we can get to Phase Two quickly! Subgroup work may need to consider both patientspecific and population level data: may dovetail with SIM evaluation work There are various ways to break out data getting information on patients who are part of a tribe or ethnic group isn t captured by county or ZIP code

6 Feedback: Multi-Payer Alignment Task Force Overall, pleased with the progress made thus far Interested in the connection between goals, questions to answer, potential value, and data analytic elements How to use data for business purposes is key Providers want to help solve the cost and outcome pieces of the question

7 Feedback: Multi-Payer Alignment Task Force (continued) Standardization of information from payers is very important Keep eye on other State efforts in this sphere: Administrative Uniformity Commission (MDH) E-Health Advisory Committee

8 Review of Definitions of Key Terms Common data analytic elements Data analysis Data analytic element (or component) Data sharing Data source Meta-information about the data analytics Raw data Are there other basic definitions that are critical to this work?

9 Review of Basic Assumptions for Phase One What can be done in today s environment (ACO focus, position for Phase Two) Data analytics is limited by available data sources Data analytics aren t used when not shared, people aren t aware of it, or don t have skills to understand and apply it Data analytics are created by: payers; some providers; public-private alliances; and, public health agencies (abundance for some; gaps for others) No standard content or format exists to enable organizations to produce aligned or consistent data analytics Are there other basic assumptions that should be noted?

10 Discussion of Data Analytic Elements and Data Sources: Homework Tight turnaround between last meeting, including the Thanksgiving holiday. Thanks for your hard work! Homework feedback grouped by the Triple Aim: 1. Better Care* Improving the patient experience of care, including quality and satisfaction 2. Better Health Improving the health of populations 3. Affordability Reducing the per capita cost of health care *Bold = National Quality Strategy 3 Aims; Plain text = IHI Triple Aim

11 The Charge to the Data Analytics Subgroup: Prioritized Data Components Provide recommendations to the Multi-Payer Alignment Task Force and the Community Advisory Task Force on a set of common data analytic elements that should be consistently provided by and/or made available to payers, providers and other stakeholders involved in shared accountability arrangements.

12 Discussion of Data Analytic Elements and Data Sources: Questions Which elements are duplicates (can be consolidated)? Which elements belong in Phase Two? Which elements should be in the Set of Common Data Analytic Elements recommended in Phase One? Is anything missing that should be in the Set of Common Data Analytic Elements for Phase One?

13 The Charge to the Data Analytics Subgroup: Guiding Principles Create guidelines and principles to motivate and guide greater consistency across the data analytics shared among public and private purchasers, health plans, other payers (e.g., TPAs), and providers in order to support shared accountability for improving quality, cost, health outcomes and consumer experience. Proposal: Five to seven distinct guidelines that ensure that each of the Triple Aim goals are addressed

14 An Example of Guideline Development Health Care Home Payment Methodology Principles Developed in 2009; a number of Subgroup members participated Examples: (three out of the ten principles) 6. Providers will prospectively self-identify patients eligible for care coordination payments, and notify payers using a common multipayer method that utilizes information on medical and nonmedical complexity. 7. Care coordination services will be coded consistently across practices and payers, fostering uniformity in definitions of the duration of service, level of patient complexity, etc. 10. The care coordination payment methodology will be collaboratively refined and will evolve over time.

15 Potential Actions to Help Motivate and Guide Greater Consistency Define and communicate to payers, providers, and other stakeholders the business case for, and the value that will be derived from, alignment of data analytics approaches and core elements. Agree on a common set of outcomes Select data analytic elements that are need to have vs. nice to have Select data analytic topics that are not difficult, expensive and/or overly burdensome to calculate, share, interpret and use Increase general awareness among providers regarding the value of the data analytics they receive or will receive, including how to use that information to improve quality, cost and health outcomes Get key stakeholders on board with applying the principles and providing the set of recommended data analytic elements

16 Brainstorming Guiding Principles Building on the discussion from our first meeting What basic guiding principles should be followed by those who provide data analytic elements? What basic guiding principles would make it easier for those who provide data analytic elements to choose to align their efforts?

17 Next Steps Meeting 3 Homework Assignment Draft Report-Out for Task Forces Remaining Topics to Cover in the Final Phase One Meeting Finalize recommended Guiding Principles Finalize recommended Set of Data Analytic Components with identified data sources Suggested best practices for data analytics: What are they? Or, what approach should be used to identify them? A Data Analytics User Guide outline: What should be included?

18 Data Analytics Subgroup Final Phase One Meeting Dates Monday, February 9, p.m. 3 p.m. Hiway Federal Credit Union 840 Westminster Street, St. Paul

19 Public Comment

20 Contact Information Data Analytics Subgroup Dr. Rahul Koranne Chair Heather Petermann DHS Krista O Connor (Krista.Oconnor@state.mn.us), DHS Facilitation Team Diane Stollenwerk (diane@stollenwerks.com) Christian Heiss (cheiss@chcs.org)