Identifying and Reducing Variation in the Supply Chain

Transcription

1 Identifying and Reducing Variation in the Supply Chain April 14, 2015 Yohan Vetteth, MBA VP of Healthcare Data & Analytics Kari Bignell Manager, Data Architecture DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not necessarily represent official policy or position of HIMSS.

2 Conflict of Interest Yohan Vetteth, MBA Kari Bignell Has no real or apparent conflicts of interest to report HIMSS

3 3 Learning Objectives Discuss the multidisciplinary approach used by collaborative care teams to implement an analytics platform that helps ask better questions to facilitate clinical and financial improvements Evaluate the advantages of an adaptive data warehousing approach to build applications for clinical and business analytics Discuss how the deployment of the Supply Chain Discovery Application has the potential to improve health outcomes for patients, cost savings, and operational efficiency by reducing variation in care

4 4 An Introduction to the Benefits Realized for the Value of Health IT

5 Stanford Health Care 5

6 6 Stanford Health Care v2014 v10

7 7 Clinical & Business Analytics Stanford Health Care Information Technology Services 2012 Established 5 Employees Clinical & Business Analytics EDW 2015 Current Day 35 Employees Reporting Analytics Advanced Analytics Prioritized data-driven quality improvement initiatives Apps designed for self-service discovery and metric tracking Strong clinical and business context Agile project development for evolving user needs

8 8 Guiding Principles Make information more accessible Avoid waste Single source of truth Secure data

9 9 Iterative Multidisciplinary Approach Define cohort criteria Define measurement metrics Clinicians Deploy evidencebased interventions Support Services Standardize and improve workflow Evaluate outcomes measurements Evaluate clinical relevance Test improvement hypothesis Modify as relevant

10 Agile Approach Established 2012 Ad-Hoc Traditional BI Adaptive Data Warehouse Focus Need driven Technology driven Clinical outcomes Data No consistency Most data modeling upfront ONLY relevant data tables aggregated Design Fragmented Original requirements Flexible Time to Market Not always accurate Multiphased 4-6yr start-up period New functionality along the journey 10

11 11 Architecture Metric & Discovery Apps Clinical & Functional Data Marts Reporting Enterprise Data Warehouse Clarity Epic Lawson Source Systems

12 12 Analytics Journey Lack of governance causes confusion and loss of credibility Multiple reporting sources Inconsistent data definitions = Fragmented

13 13 Analytics Journey Standardize technology architecture Build strong data access and guidelines Avoid data staging through extracts Strong data governance

14 14 Data Literacy Metric Apps have specific business needs and require strong business or clinical context to enable using the app Discovery Apps empower the users to learn about the subject area with little to no business or clinical context

15 15 Paradigm Shift to Accountable Care FROM Variation in care Fee for service Segmented care TO Evidence based standards of care Pay for performance Coordinated care continuum Sick care Vague and unmanageable cost structures Well care Cost containment and transparency How do we make healthcare less expensive? The levers are ugly. We can pay doctors and hospitals less; or we can get them to reduce waste. Dr. Atul Gawande

16 16 Hospital Supply Chain Supply expense growth outpacing all others Supply costs are growing faster than wages or benefits, driven by the rampant proliferation of expensive devices. Physician preference items (PPIs) now account for 60% of med/surg spend, compared with 40% a decade ago. More than half of orthopedic procedures now use implantable devices, as do more than one-third of cardiac procedures As hospital consolidation continues creating consistency within the supply chain across the continuum of care will become increasingly influential to the bottom line Supply-side economics Purchasing practices at hospitals and health systems continue to evolve, with the supply chain continuing to be a target for large non-labor cost savings Incentives to build more accountable care-focused capabilities have given health systems good reason to evaluate their total cost structure

17 17 Finding Value How much are we wasting? How much does our procedure supply use vary between surgeons? How accurate are our preference cards? How often are we running for supplies during surgery? SUPPLY Lawson Accuracy of par levels DEMAND Epic Accuracy of preference cards What is our average spend for a procedure? Over processing Poor pricing Wasted Items Expedited demand

18 18 Finding Value SUPPLY DEMAND Lawson Epic EDW SUPPLY + DEMAND SUPPLY CHAIN DISCOVERY APPLICATION Variation Insight Waste Reduction Cost Awareness

19 19 Approach to Improvement Cohort: Single and elective surgeries Focus: Identify high cost & unnecessary variation Challenge: Balance those with high cost & unnecessary variation with cultural readiness and engagement to drive outcomes and improve quality

20 Coefficient of Variation 20 Understanding Usage Variation Biggest Opportunity Low Cost Low Variation Mean Supply Cost (or Quantity) Metrics Influenced: Supply Expense per Adjusted Patient Day

21 21 Measuring Variation - Direct Cost 2 x 2 representation of the Procedures performed illustrating greatest opportunity Scorecard trending of Procedures ranked by opportunity

22 Total Unique Items Used Understanding Item Variation Biggest Opportunity Mean Unique Items Used Metrics Influenced: Number of Items (SKUs) Inventory Turns Number of Preference Cards Item Pricing 22

23 23 Measuring Variation - Distinct Items Focus in on highest variation by Item Group Types Search for items to see how many surgeons are using an item and when they used it last

24 24 Pilot Project - ENT Department? Pilot Focus: Otolaryngology Department Approach: Iterative, Rapid Development Goal: Two concepts, one view

25 25 Surgery Basket Compare how many of each item is used and the different types of items used across surgeons for a specific procedure

26 26 Implement Intervention Utilize the discovery app to: Facilitate conversations Identify improvement areas Pinpoint items for removal from preference cards and item master

27 27 Analyze Results Use Lean processes to help: Update and streamline the preference card system Establish a solid baseline to enable improvement measurement

28 28 Conclusions Evaluate the advantages of an adaptive data warehousing approach to build applications for clinical and business analytics Driven by clinical outcomes and not by the IT bling Supports a lean culture of continuous improvement that requires agile and flexible solutions where requirements are constantly changing Highly scalable design provides faster results for future needs

29 29 Conclusions Discuss the multidisciplinary approach used by collaborative care teams to implement an analytics platform that helps ask better questions to facilitate clinical and financial improvements Embedding IT with the clinicians and support services provides a holistic approach and prevents rework Spend more time understanding the problem the real problem versus just filling the specs! CBA members are viewed as content experts in the subject matter, not just IT

30 30 Conclusions Discuss how the deployment of the Supply Chain Discovery Application has the potential to improve health outcomes for patients, cost savings, and operational efficiency by reducing variation in care Transparency around item usage for similar procedures and the challenges preference items bring Providing a mechanism to facilitate the appropriate dialogue around variation of items and usage Conversations driven by clinicians around what items to use enables stronger engagement and cost containment

31 31 A Review of Benefits Realized for the Value of Health IT

32 32 Questions Yohan Vetteth, MBA VP of Healthcare Data & Analytics Kari Bignell Manager, Data Architecture