Topics
Why develop a strategic plan?
Key components of an analytics strategic plan
Typical planning process
Key stakeholder to involve
Why analytics in healthcare?
1. Increase speed of decision making
2. Increase confidence in the decisions
3. It is at the crux of identifying opportunities and measuring
progress
McKinsey, 2012:
If US healthcare used data to drive efficiency and quality… could see more than $300 billion in value annually. 2/3 from
reducing expenditures by ~8%.
Gartner, 2007:
Only 7% of data is used for analysis in hospitals
InformationWeek, 2012: 52% organizations have
completed or are working on
Why analytics in healthcare?
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
Meet Regulatory Requirements Manage Digital Patient Data Improve Care Increase Clinician Efficiency Reduce Costs Improve Collaboration Among Clinicians Improve Collaboration Among Clinicians and Patients Personalized Medicine Share Data with More Than One Provider
Around the industry
There are pioneers and leaders we can learn from
Intermountain, Geisenger, Mayo, Partners, others
They have shown us analytics is a journey, not a destination
Their efforts have evolved over many years
Their toolsets and their staff have grown and evolved with the
organization’s appetite for information and analytics skillsets
Achieving leading, enterprise-class analytics capabilities is no
small undertaking
e.g., UPMC recently announced a 5-yr, $100M enterprise analytics
Why develop a strategic plan?
Source: Alice's Adventures in Wonderland
Would you tell me, please, which way I ought to go from here? That depends a good deal on where you want to get to
Why do you need an analytics strategic plan?
Analytics, data warehouses (DW) and clinical & business
intelligence (C&BI) are complex
There are multiple technical components and processes They impact multiple parts of the organization
Data quality is always an issue There are competing priorities
It is easy for these efforts to fail - spend scarce capital and gain
little value
Being successful requires
A shared vision
Focus on the organization's strategic goals and priorities
A strategic plan helps answer critical questions
Where do we need to go with our analytics capabilities?
In the next 12-months? In the next 3-5 years?
How are we currently doing?
Where to we need to improve our people, processes, and technology to
get where we need to be?
Given limited organizational resources (both time & money),
where should we start?
What are the most urgent needs?
What projects have the most strategic value in the future?
What resources will it take to get where we need to be?
Key components of an analytics strategic plan
Vision & goals
Gap analysis
People: staff, skills, organization structure
Process: project governance, data governance, support
Technology: data management, data quality, information delivery
High-level project needs & use cases
Criteria for prioritizing projects
What data is needed to support the identified projects?
Cultural barriers and challenges
Recommendations
Cost/Benefit Analysis
Typical strategic planning process
• Stakeholder education• Vision & goals session • Stakeholder interviews
• Identify use cases & data needs
Needs
Assessment
• Current State & Strategic Information Systems Plan alignment • Technical assessment
• Org. structure, staffing, and skills assessment
• Data/project governance, information management assessment
Gap Analysis
• Cost estimates
• Benefits/ROI assessment
• Identify project/use case dependencies & constraints • Develop roadmap
Road mapping
• Develop recommendations
• Review & revise Roadmap with key stakeholders • Assemble resources for implementing Roadmap
Involve key stakeholders throughout the process
Executives Business Operations Clinical Operations Quality Research Information Technology Inpatient Ambulatory Ancillaries Service Lines Revenue Cycle H.R. Finance Supply Chain Data Warehouse PMO/Architects Apps Teams Report Writers Clinical Health Services Critical Success Factor: Collaboration of Leaders & AnalystsProgression of Analytics & BI
Com pe ti ti v e a dv a nt a ge What happened?What if these trends continue? Why is this happening?
What actions are needed? What exactly is the problem? How many, how often, where? What will happen next?
What is the best that can happen?
Analytics Access and reporting Standard reports Forecasting/extrapolation Statistical analysis Alerts Query/drill down Ad hoc reports Predictive modeling Optimization
Data quality, KPI, business performance
management, scorecards
Master data, enterprise data management, data stewardship,
enterprise metadata
Data quality, data consistency, data security, privacy
Raw data, spreadsheets, databases, reports
Where are you now? Where do you want to go?
Adapted from Villar & Kushner (2010). “A Framework to Map & Grow Data
Data as a strategic asset, real-time alerts &integrated analytics
Where are you now? Where do you want to go?
Level 7 Personalized medicine: Integration of genomic, familial, text, and patient self-reported data used for
predictive modeling, preventive care and wellness management.
Level 6 Waste elimination: The focus is in maximizing quality and minimizing cost of production. Complex modeling
and forecasting is readily available. Data from ACO partners and claims is integrated with patient specific costing and claims data and used for identification and elimination of variability & waste in the complete, end-to-end care process.
Level 5 Cultural data literacy: Permanent technical and clinical improvement teams in-place for top 10 conditions; at
least 60% of employees have access to KPIs actionable to their role. Analytics are embedded in the EMR to affect clinical & financial improvements at the point of care.
Level 4 Evidenced-based population management: Patient registries for at least the top 10 patient conditions within
the organization, supporting acute & chronic condition mgmt; measurement of clinical guideline usage; and clinical research
Level 3 Automated external reporting: Regulatory and other reports such as Value-based Purchasing, PQRS, MU;
accreditation/regulatory such as JCAHO, ACC, STS, HEDIS. Adherence to industry standard vocabularies are required at this Level.
Level 2 Automated internal reporting: Key performance indicators, highly interactive dashboards and reports that
allow for effective hospital and clinic management and business modeling are available.
Level 1 Vocabulary, metadata, & data governance: Searchable metadata repository, core data elements linked with
standardized naming and data types. Data governance & stewardship processes in place.
Level 0 Core data integration: As a minimum – EMR Level 3 data, Revenue Cycle, Financial, Costing, Supply Chain, and
Patient Experience integrated into a single data warehouse.
Gap Analysis: How do your capabilities compare?
Dimension Poor Achievement Level Average Leader Typical Challenges
Strategic & Operating
Plan No integrated plan; project by project funding & design
Culture of Analytics
Analytics skills concentrated in a small set of people, limited senior leader sponsorship, disparate
acceptance of data-driven decision-making and planning
Data Governance Limited standards, no data dictionary, data linage
unknown
Data Quality Conflicting numbers, incomplete data, significant delays in data availability, varied levels of standardized terminology
Data Capture Little standardization in key master data and underlying
terminologies, disparate edits
Data Accessibility Silos of data, manual data integration required,
unstructured data 'trapped'
Information Delivery Highly manual; Requires 'expert' users, inconsistent
amd siloed tools
Support Services Uncoordinated and inconsistent support resources, tools and SLA's across silos Technical Architecture Lack of standard platforms and maintaining version and
Gap analysis food for thought
Business/
Clinical
IS
Data Stewards Super Users Subject Matter Experts Data Managers Project Managers Infrastructure Support DBAs Executive Oversight Analytics Operations Subject Workgroups Executive Sponsor• Do your current org structure and processes facilitate collaboration between IS & the business/clinical personnel?
Gap analysis food for thought
Data Distribution Data Integration Security/ Privacy Data Stewardship Data Quality Master Data Management Metadata Management C han ge M an age m en t• Do you have defined processes for key areas of data
governance?
• Are they used? Are they effective?
D
at
a G
over
Needs assessment tools
For each use case identified, which user groups do they support? What is the benefit? How do they support business & clinical priorities?
For each use case identified, which what data is needed to support it?
For each data source, how is the data quality? How hard will it be to clean up? Users Use cases Benefit
Needs Matrix
Use Case Data source Data QualitySource Matrix
Other questions to consider
Do your core vendors have analytics solutions?
Should you standardize on one as the Enterprise analytics solution? How do we avoid creating new data silos?
What information architecture is needed to integrate data
Other questions to consider
What data governance processes and information management
tools are needed to
Improve data quality?
Standardize on common metrics, definitions, and master data?
What initial use case(s) are the best place to start?
Data is available, lower technical complexity, high value to the
organization
Best practices for implementation
Use an incremental, project-based approach to build toward
the long-term vision
Address analytics foundational needs in parallel with initial
projects
Data architecture
Standardization, data governance Processes, roles, & responsibilities
Data quality is an on-going process, not a one-time project
Include initiatives to grow analytics knowledge & skills across
the organization
Remove cultural barriers to success
Rome wasn’t built in a day
Achieving the vision will take time
The Roadmap will be a valuable tool for communicating with
stakeholders
The implementation roadmap should be built to enable
demonstrating tangible progress while you build toward the
vision
You should have deliverables every 3-5 months in order to demonstrate
progress and maintain buy-in
Start with simpler projects
Integrate initial, high-value data sources Gain lessons learned
Recommended Practices
For information delivery projects
Define a reasonable scope including 1-3 data sources and a core set of
dashboards/reports to build
Address data quality and standards for the selected subject area
Initial data cleanup as part of the project
Identify a data steward(s) for on-going cleanup & support
Identify project dependencies
Some projects cannot be completed until foundational issues are
addressed
Information delivery projects are unlikely to be successful until
operational processes are defined and implemented
Data quality, standards, and operational processes are critical to user adoption
Example Project Prioritization Criteria
Integrates a high demand data source(s)
High demand = supports lots of use cases or used by many groups
Ease of integrating data source
Technical complexity Data quality
Existing standards
User readiness and buy-in
Benefit/Impact
Business/clinical impact of supported use cases
Example Roadmap – “One Size Doesn’t Fit All”
Year 1 Year 2 Year 3
Governance (Continuing)
Data Quality (Continuing)
Communication Plan (Revise for new BI Tool and ACO focus)
Early Projects
• Leadership
• Prioritize Projects
• Roadmap approval • Establish Accountability
• Categorize and prioritize • Quick wins
Long Term Projects (Focused on ACO –Continuum of Care)
Architecture Design
Example Roadmap – “One Size Doesn’t Fit All”
Y1 Y2
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Governance
Operating Plan
Define roles, procedures, policies Implement operating plan Ongoing evaluation & optimization
Communication
Training & Education
Data Architecture
Arch planning & design Select/purchase ‘gap’ tools
Analytics project-based implementation Ongoing support & tuning
Data Quality & Source Data Projects
Provider Master
MPI
Pt Registration (address, PCP/referring
provider)
Cost Accounting
Clinical documentation initiatives
Analytics Projects
Project 1 Scope, Design, Build, Rollout Project 2 Scope, Design, Build, Rollout
Where to start for your strategy & roadmap
Organize for success
Who owns the Strategic planning process? Who is the executive sponsor?
What key stakeholders need to be involved?
Educate for success
What analytics is and why it is important to YOUR organization Analytics is an on-going initiative, not a short-term project
Questions & Discussion
Paula Edwards, PhD [email protected]
