This document discusses using an agile and flexible approach to healthcare enterprise analytics. It emphasizes using analytics to drive quality improvement by involving all parts of the organization. The approach focuses on clinical outcomes and aggregates only relevant data to support prioritized improvement efforts. New functionality is delivered incrementally along the journey. The goal is to standardize workflows, investigate trends in clinical data, and deploy evidence-based knowledge at the point of care to define populations and key indicators. This is part of an organizational journey involving increasingly sophisticated levels of analytics focused on costs, reimbursements, and managing populations' health.

1. Healthcare
Enterprise Analytics
Yohan Vetteth
VP Healthcare Data and Analytics
Stanford Hospital and Clinics
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2. Key Takeaways
Focus
- Data driven quality improvement
- Integration of Lean approaches to ensure sustainability
Approach
- Unlike other industries, healthcare needs an agile flexible approach
- All facets of the organization needs to be involved (Physicians, Nurses,
Administrators, Quality, IT, Performance Excellence, Finance)
- Analytics is a journey, not a destination
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3. Strategic, outcome driven, agile approach..
Traditional BI Approach Practical Approach “Late Binding” Approach
Technology driven Need driven approach Clinical outcomes focused
approach approach
Develop data model for Get data where you Aggregate only relevant data to
all systems up front can support the prioritized clinical
improvement efforts
Design based on Design? Flexibility to address specific
currently known needs
information needs
Typical 3+ year start-up Quick but not always New functionality delivered
period accurate along the journey

4. Involve all stakeholders in analytics approach
1 2
Standardize/improve MD MD Deploy evidence-
workflow including based knowledge
clinical RN PE RN assets at the point of
care (e.g. Order Sets,
DA
PE Intervention
Clinical &
IS Business IS QA Indications)
QA
Process
Improvement
4 Outcomes
Implementation 3
Analysis
Investigate causal Define population
relationships, trends criteria and key
and predictive patterns PE indicators with
in clinical data, DA DA clinical artifacts
evaluate clinical QA found in the data
IS
relevance, and test
RN
improvement MD PE
hypotheses FA MD
RN
MD = Physician Lead; RN = Nurse Manager; DA = Data Architect; QA = Quality Analyst;
PE = Performance Excellence; FA = Financial Analyst; IS= Informatics Specialist
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Copyright: Stanford Hospital and Clinics; Health Catalyst

5. Healthcare Analytic Adoption Model – It’s an organizational journey!
Level 8
Cost per Unit of Health Reimbursement & • Contracting for & managing health
Prescriptive Analytics
Cost per Capita Reimbursement & • Taking more financial risk & managing it
Level 7 Predictive Analytics proactively
Cost per Case Reimbursement • Taking financial risk and preparing your
Level 6 & Data Driven Culture culture for the next levels of analytics
Clinical Effectiveness & Population • Measuring & managing evidence based
Level 5 Management care
Level 4 Automated External Reporting • Efficient, consistent production & agility
Level 3 Automated Internal Reporting • Efficient, consistent production
Standardized Vocabulary & Patient
Level 2 Registries
• Relating and organizing the core data
Data Integration– Enterprise Data • Foundation of data and technology
Level 1 Warehouse
Level 0 Fragmented Point Solutions • Inefficient, inconsistent versions of the truth
Source : Dale Sanders, Senior Research Fellow, The Advisory Board Company