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Delivering Value for Patients and Payers
Data & Measurement
Day 1 – Morning
Michael J. Deegan, M.D., D.M.
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DATA
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Health Care Data Essentials
Sensitivity: the ability to identify a condition
Specificity: the capacity to correctly identify a condition
Timeliness: the availability of data relative to the time of the event
Availability: the ease with which the data is accessed or captured for use
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Data “Cleansing”Validate & Normalize
Align data with a single condition or diagnosis; understand it’s distribution
Clean & Validate
Remove erroneous, incorrect data elements
Extract & Validate
Understand its relevance to condition
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Pitfalls that Inhibit Using Data to Drive PI Lack of timeliness
Reports based on readily available non-relevant data
Failure to adequately analyze and interpret the data
Poor assignment of accountability for indicator performance or improvement
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Pros & Cons of Data Types
Claims Data• Available
• Untimely
• Fragmented
• Insensitive
• Lacks specificity
Clinical Data• Sensitive
• Specific
• Timely
• Longitudinal
• Uneven availability
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Gaining Insight by Using MultipleData Types
Clinical data
Socio-demographic data
Care management data
Claims data
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Non-Clinical Risk Factors
VitreosHealth® Predictive Model
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Uncovering Meaningful Data Patterns
Identify the most seriously ill
Appreciate the influence of socio- demographic & clinical risk factors
Measure cost & outcomes (Value)
Discover cost & utilization drivers
Identify “deviants” ( positive & negative)
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Applying Predictive AnalyticsTo Achieve Greater Value
Most common conditions within population
Patients utilizing the most resources
Most effective treatments
Most effective and efficient caregivers
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“Hidden Opportunity”
“Unknown/Relatively Healthy”
“Critical”
“High Utilizers”
Stat
e-of
-Hea
lth (S
OH)
Risk
High
Low HighMember PMPM Costs
Population Analysis Framework
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A Contemporary Example of Predictive Analytics – VIDEO
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MEASURES
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Deciding Which Measures to Select
> Balanced mix using CVC framework
Patient-oriented process & outcome metrics
Reflects population(s) at risk → practice panel
High leverage to close a care gap
Regulatory or performance requirement
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Measure Selection – Practical Issues Data source availability? - administrative - clinical - survey
Ease of capture?
Comparative data – internal, external?
Benchmarks – risk or case mix adjust?
Inclusion – exclusion criteria defined
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Examples of Measure Types Process - Health Risk Assessment - Colorectal cancer screen - Anti-platelet rx for CAD patient Outcome - Intermediate → HgbA1c - End of episode - Acute → post – MI return to work - Terminal → mortality rate Patient Experience - Access - Overall
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CMS – Measure Types
∙ Process∙ Outcome∙ Intermediate Outcome∙ Patient Reported Outcome∙ Efficiency∙ Cost / Resource Use∙ Structure∙ Composite
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Process of Care Measure Criteria*
1. Sound evidence base the care process leads to improved outcomes
2. Measure accurately captures whether the E-B care process has been provided
3. Process measure has few intervening steps before outcome is realized
4. Measure implementation is unlikely to have unintended consequences
Chassin et al. NEJM, 23 June 2010
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MUC ID Measure Type Program
MUC15-439 Testing for uterine disease prior to obliterative procedure
Process
MIPS
MUC15-1019 Non-recommended PSA-based screening
Process MIPS
MUC15-229 Hep C virus – sustained viral response
Outcome MIPS
MUC15-411 Patient reported outcome > ileo-femoral stent
Patient Reported Outcome
MIPS
MUC15-576 Prevention quality indicator composite
Composite
MIPS, MSSP
CMS- 2015 Measures Under Consideration
See Handout inCoursebook
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Desirable Outcome Measure Features
Outcome Metrics
· Condition specific · Multi-dimensional · Span full care cycle
Cost
· Total costs for full care cycle for condition
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The Outcome Measures Hierarchy*
Long term consequences of treatment
Duration – strength of recovery /Recurrences
Disutility of care or treatment process
Time to Recovery / Normal Activity
Degree of Health or Recovery
Tier 1Health Status Achieved or Retained
Tier 2Process of Recovery
Tier 3Sustainability of Health
Survival
*Porter ME: N Engl J Med 363: 2477, 2010.
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Outcomes Hierarchy – an Example*
Hip Replacement Surgery
Tier 1 – Health Status Achieved – Retained
·Survival………..Mortality rate (inpatient) ·Post-rx care…..Pain management …..Level of physical activity
Tier 2 – Recovery Process
*Porter & Lee: HBR, Oct 2013.
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·Time……..to start treatment …….to return to full physical activity …….to return to work / play (e.g., golf) ·Care Processes…..delays & anxiety …..pain during treatment …..time in hospital …..complications
Tier 3 – Sustainability
·Maintenance of functional status ·Need for revision – replacement ·Long-term consequences
Porter & Lee, HBR, Oct 2013.
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APPLICATIONS
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Measure Selection - Overview
> Balanced mix using CVC framework
Patient-oriented process & outcome metrics
Reflects population(s) at risk → practice panel
High leverage to close a care gap
Regulatory or performance requirement
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Clinical Value Compass*Functional- Physical³- Mental³- Risk status¹
Clinical-Prevention¹-Screening¹-Diagnosis¹-Rx Monitoring¹- Morbidity¹- Mortality¹
Patient Experience- Services -Overall satisfaction³ -Access³- Health benefit(s)³
Cost to Patient- Direct medical²- Indirect personal - social³
modified from Nelson EC, et al. Measuring Outcomes & Costs: The Clinical Value Compass in Practice-Based Learning & Improvement, 2007, JCAHO .
¹ practice report² billing data³ patient self report
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BalancedScorecard
Payer – Patient Mix
PatientExperience
ClinicalProcesses &Outcomes
FunctionalOutcomes
Cost toPatient
Financial Performance
- Access- Overall satisfaction score- Retention rate
- Screening- Prevention- Monitoring- Safety Measures- Care Outcomes
- Full recovery of ability- Return to work- Activities of daily living
- Direct- Indirect, e.g., inability to return to work
- Traditional business – percent & margin- Risk contracts – percent & margin- Operating expenses
- Private / Public (%)- > 65 yo / < 65 yo (%)
Balanced Scorecard for Monitoring Practice
VH MS – MJD – 8 - 14
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Population Health Analysis Framework
“Hidden Opportunity”
“Unknown/Relatively Healthy”
“Critical”
“High Utilizers”
Stat
e-of
-Hea
lth (S
OH)
Risk
High
Low HighMember PMPM Costs
Socio-Economic Related?
Non- Compliance?
Access-to-Care?
Perceived Mental Well-being?
Phase 1 Focus
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QUESTIONS / COMMENTS
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Physician-led Population Health Maturity Cycle
(Where are You?)
FIN
ANCI
AL C
ON
TRIB
UTI
ON
& M
ARGI
N IM
PACT
POPULATION HEALTH MATURITY
Phase II Phase IIIPhase I Phase IV
COPYRIGHT © VITREOSHEALTH ALL RIGHTS RESERVED.
Programs Provider-Driven Patient-EngagementPilots: Payer-driven
S1
S4S7
S6
S5
S2
S3
S1, S2, S3….S7 are Physician Groups
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Factors Driving the Provider-driven Population Health Maturity
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Reliance on Payers
Internal Capabilities
1 2 3 4
Level ofInfluence
PHASES
Progress Toward Functioning as a High Performing PHM Provider Organization
VH MS – MJD – 8 -14