Current and Future State of Analytics: Implications for Provider Organizations May 10, 2012

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Agenda • Methodology • Environment • Findings • Implications for Provider Organizations

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Methodology • Interviews with health care thought leaders (CIO,

CMIO, CMO, CEO) • Distilled feedback • Authored white paper sponsored by SAS

Institute

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Interview Cohort Profile

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$50B of Thought Leadership

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Research Triangle

FORCES

IMPERATIVES RESPONSES

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Research Triangle

FORCES

Austerity Regulation Technology Demography Safety/Quality

IMPERATIVES RESPONSES

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Research Triangle

IMPERATIVES RESPONSES

Consolidation/Scalability Quality/Safety “Volume to Value” Population Health New Payment/Business Models Advanced Decision Support Lean Production

FORCES

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Physician Alignment New payment model pilots Focus on clinical integration RT/Predictive Analytics Distributed Analytics “Strange bedfellows”

IMPERATIVES

FORCES

RESPONSES

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Environmental Assessment

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Strange Bedfellows

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Questions • What keeps you up at night? • What are your top 3-5 strategic priorities (next 18-24 months)

and level of readiness or preparedness for them? • How do you define analytics? • Do you see a need for analytics changing over the next five

years? • Of the data that informs decisions, how much is retrospective

and how much is real time? Do you see opportunities for using data to make predictive decisions about care, consumption, or spending?

• How are you viewing your organizational structure relative to health reform, and how, if at all, will that change over the next five years?

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Top Leadership Priorities • Source-system integration

• “Following the patient”

• Patient safety at the forefront

• Payment and delivery reform: both driven by regulation and market forces • “Measured Care”

• “Shared Accountability”

• The population paradigm – Joslin Diabetes Center

• Productivity and cost

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“Defining” Analytics • Taking individual points of data and being able to look for or identify

patterns and trends that are of significance • Systems that present data in aggregate against models,

expectations, trend lines and exception reports in order to identify opportunities for improvement, systematic issues

• Runs table from simple table production to complicated statistic analysis

• Measure what matters • Build algorithms based on learning experience and respond to

different perspectives of data

• Beyond information and into knowledge, and hopefully wisdoms to the C-suite

• Tools to derive meaningful information 14

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Tiers of Analytics: Dr. Shultz’s* View

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LEVEL II Multi-dimensional

Analysis

LEVEL I Ad Hoc Data Queries

Intersections between disease groups, locations, clinicians. Historical + real time. Middle managers

Detecting variations of care, behavior, and outcomes. Real time + predictive. Mathemeticians and statisticians (“gearheads”)

Pull summary totals, i.e. number of diabetics in a certain period. Recurring reports, historical. Front-line staff

LEVEL III Expected vs. Observed

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* Director of Information Technology Integration at Vanderbilt University Medical Center

Convergence: Clinical/Financial Integration • Holy grail

• Desire for “one pipe”, “one source”-for clinical, admin, financial data

• Not only about adoption of EHR, but the deeper, richer use of EHR

• Risk contracting/bundles expediting convergence

• Moving from operational performance improvement, to integration of a continuum, to managing populations

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Triple Aim • “Analytics is the future: information is the tradable

commodity”

• Cost and quality are no longer a separate parts of the equation

• Shifting from widgets/units of care to desired outcomes

• Health care overbuilt, need to reduce costs, duplication: must gain efficiencies and increases virtualization

• Revenue centers will now become cost centers

• ABC is top of mind

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User Interface • Physicians

• “We recognize that for us to help motivate physicians to change, we are going to need information to make a compelling case. We do not tell doctors what to do, we show them what they are doing. Analytics is the hallmark of how we manage physicians.” -Dr. Simeon Schwartz, President and CEO WESTMED

• Leadership • Beginning to want more and newer types of information

• This is no longer an “IT project”

• Desire to build data driven organizations

• Bedside • Need to enable information on the fly

Must be nimble and adaptive

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Organizational Impact of Analytics • Impacting job roles

• CIOs: from order takers, to facilitators, to innovators

• The Burgeoning CMIO: from “techno-doc” to legitimate differentiator in practice of medicine

• Focused Factories • Hopkins Armstrong Institute – Peter Pronovost, M.D.

• Enterprise analytics team-building one source of truth for the entire organization when it comes to data mining

• Ramping hiring • Directors of analytics

• Increased analysts

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Revenue Reality • “Sawing limbs, not trimming leaves”

-Rick Schooler, CIO Orlando Health

• Capitation, global payment, bundled payment

• Organizations bullish on changing payments are aggressive in pursuit of data and analytics to drive change

• “Reality tells us we will have to be at 80% of Medicare” -George Conklin, CIO Christus Health

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Aging of Data

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Retrospective

Real Time

Predictive

• 95% historic-5% Current and less than 1% predictive

• All the good stuff is at least 6 months old

• Good for problem identification

• The real time dashboards are the ones that mean something

• Real time becoming more dominant

• Retro + Real: Use Case of VAP

• What can predictive modeling nail: readmissions regardless of disease state, transactional

• Predictive models are all short because a lack of clinical data

• Use Case: making a choice between stent, drugs, device, etc. and being able to show someone outcomes and costs

Next Frontier: Big Data • Genomics • NLP • Social media • EHR depth

• Biggest challenge is learning from and utilizing information generated

• Big data in health care is NOT big data compared to other industries

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Headwinds • Near term attention focused on:

• Health reform

• Core systems

• Meaningful Use

• Integration

• Other regulations: ICD-10, HIPAA 5010, etc.

• Analytics has currency, though it is competing for mind and wallet share

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Timing for Analytics to Take Hold

>3 WINDOW

<7 Handful of “mad scientists in analytics” Market still very immature, needs vision

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Implications • Investments in analytics are increasing • Application of analytics in provider organizations

is embryonic • Source data quality and integrity is critical • Payment model redesign is a major driver

• Population health is a new paradigm • Cost is very important

• Human capital: analytics IQ needs to increase 25

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Thank You

Don McDaniel 443.904.2882

dmcdaniel@sage-growth.com

A link to the research

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