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Transcript of Leveraging Clinical Data for Risk Adjusting Bundled...
Leveraging Clinical Data for Risk Adjusting Bundled Payments
April 13, 2015
Suma Thomas, MD, MBA, FACC Soyal Momin, MS, MBA
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.
Conflict of Interest
Suma Thomas, MD, MBA, FACC Soyal Momin, MS, MBA Has no real or apparent conflicts of interest to report The study was sponsored by AstraZeneca Pharmaceuticals LP
© HIMSS 2015
Learning Objectives 1. Discuss the need for risk adjustment of bundled
payments using both payer claims and provider clinical data
2. Demonstrate how to integrate payer claims data with provider clinical data for the development of the Longitudinal Patient Record (LPR)
3. Demonstrate the application of the LPR for risk adjusting bundled payments
4. Debate the need for risk adjustment of bundled payments using both payer claims and provider clinical data
The Value of Health IT • Episode based treatment can lead to an
increase in care coordination and patient satisfaction
Satisfaction
• Bundled payments promote healthcare system transformation by reducing variations in care
Treatment/Clinical
• LPR efforts provide a better understanding of how patient variables affect outcomes
Electronic Information/Data
• Episode based treatment can promote patient engagement
Prevention/ Patient Education
• Use of integrated data for the PCI research study shows applications for payment innovation
Savings
http://www.himss.org/ValueSuite
Background
• Healthcare costs account for 17.2% of the GDP* • Traditional payment methods are not associated with
better patient outcomes** • Alternative payment models are being explored that
promote quality, efficiency, and effectiveness in health care delivery
* The Commonwealth Fund, Issues in International Health Policy, May 2012
** HealthAffairs, Health Policy Briefs, October 2012
Bundled Payments
• Bundled payments employ a single payment for all services related to a treatment or condition
• Goals: • Target a common procedure with variation in costs and outcomes • Cover essential clinical items and services • Provide a financial incentive to reduce inappropriate care • Yield a “win-win-win” outcome for payers, providers, and patients • Be simple enough to administer effectively • Maintain or improve quality of care • Appear seamless to policy holders • Be scalable and sustainable
HealthAffairs (Millwood). 2009;28(5):1418–28; 10.1377/hlthaff.28.5.1418
The Problem • Episodes of care should be risk adjusted* • Traditional risk adjustment approaches rely on healthcare claims
data** • Primary Objectives:
• Integrate payer claims data with provider clinical data for development of the LPR
• Demonstrate the application of the LPR for risk adjusting bundled payments
• Secondary Objectives: • Determine pre-procedural risk factors for high episode costs • Examine the procedures and services in the PCI episodes that
lead to high episode costs * http://aspe.hhs.gov/health/reports/09/mcperform/report.pdf
**http://www.hci3.org/sites/default/files/files/Severity%20Adjustment%20Fact%20Sheet.pdf
Data Environment
Database
Transactional Systems
• Medical evidence development is a major repurposing of administrative data for health services research
• Minor repurposing of administrative data for health plan operations
• Core claims processing and adjudication
What we see is not always what we get!
Limitations of Claims Data
Limitations of Claims Data
Deep Data Required to Answer Complex Questions
Stakeholders
• Partnership: • Cleveland Clinic Foundation (CCF) and HealthCore/Anthem
(HC/ANTM) • Executed Non-Disclosure Agreement (NDA), Business
Associate Agreement (BAA), and Data Transfer and Collaboration Agreement (DTCA) in January 2013
• Percutaneous Coronary Intervention (PCI) was selected for the research study
• All parties agreed on study objectives and protocols • Guiding Principles:
• Protect patient privacy • Protect business interests of data sources • Protect integrity of research
Data Sources
Data Sources: Following minimum Personal Health Information (PHI) exchange criteria, this study included:
• A CCF PCI patient list developed by HC/ANTM and CCF • HC/ANTM healthcare database administrative payer data
including claims, eligibility, lab results, and provider information
• CCF patient episode data • CCF PCI registry and Electronic Medical Record (EMR)
data The LPR was developed by HC/ANTM
Project Management - Phases
Define Plan Project Approval Launch Manage Close
Problem & High-Level Solution Definition
Project Approval
Requirements Definition
System Design
System Build, Testing & Deployment
SDLC Phases
Patient Reconciliation
Claims/Eligibility Extracts Provided for CCF Grouper Input
PCI Registry, EMR and HC/ANTM Core Files Provided to CDI Vendor
LPR Data Researchable
LPR Output
Prelim Results
Researchable PCI Registry Data
Grouper Results Returned from CCF
Project Management - Project Timeline
Patients Reconciliation
* Before Final Filtering for Age, Insurance Product, and Eligibility Periods
Year of PCI Event
Total Unique Patients
Matched CCF & HCI
Percent Matched
Yet To Be Matched Cleveland Clinic
(CCF)
Yet To Be Matched HealthCore
(HCI)
2006 238 0% 238
2007 204 0% 204
2008 205 7 3% 184 14
2009 286 118 41% 75 93
2010 273 111 41% 89 73
2011 284 112 39% 98 74
2012 283 78 28% 145 60
Totals 1773 426 24% 1033 314
Bundle Grouper
• CCF grouped claims into PCI treatment episodes capturing utilization 30 days pre and 180 days post PCI surgery
• HC/ANTM received grouper output and replaced imputed costs with actual allowed amounts for use in research study
Historical Data*
Bundle Definitions
Bundle Engine
Reports: •Summary •Exclusions
•Complications
Bundle Editor
Step 1: Identify and Select Bundle
Step 2: Identify Anchor Records
Step 3: Gather Additional Claim
Records to a Bundle
Step 4: Finalize Bundle
1. Identify the trigger claim - Use procedure and/or diagnosis code information
2. Determine services during the time window
- Time windows defined before and after trigger service - Definition considers the relevance of timing of a patient’s services
3. Identify unexpected outcomes
- Identifies inpatient admission for medical or surgical complication
Bundle Grouper Process
LPR Development Process
Data Sources & Integration Efforts
Pseudo Coding PCI Registry/PCI Clinical
Pseudo Coding PCI Registry/PCI Lesions
Pseudo Coding PCI Registry/PCI Medications
Pseudo Coding EMR/Medication Administered
Longitudinal Patient Record (LPR) Longitudinal Patient Record (LPR) Sample data and does not reflect
an actual individual
Longitudinal Patient Record (LPR) Sample data and does not reflect
an actual individual
Sample data and does not reflect an actual individual
Longitudinal Patient Record (LPR) Sample data and does not reflect
an actual individual
Longitudinal Patient Record (LPR)
Sample data and does not reflect an actual individual
Research Study Data Assets Utilized
• Variables Unique to the Registry
• Demographics: Race
• Clinical Characteristics: Elective vs. emergent PCI, BMI
• Recent Medication Use: Beta blockers, Long acting nitrates
• Angina and Heart Failure Classifications
• Stress/Imaging Study Results
CCF PCI ACC Registry + EMR Data
HealthCore Integrated Research
Environment (HIRE) (Anthem
Members)
Enriched Data for the Research Study
PCI Bundle Grouper
Data
Study Design and Population
• Retrospective observational study design • Patients receiving PCI procedures at Cleveland Clinic Main Campus • Patients ages 18 – 75 years old at time of PCI • Study period: 1/1/2006 through 12/31/2012
Up to 30-day prior
PCI performed at CCF Main Campus
PCI episode identified via PCI episode grouper
1/1/2006 PCI Registry data + HIRE data + Grouper output 12/31/2012
Up to 180-day post
Study Population Characteristics
Characteristic Number of PCI bundles, n 388 Emergent, % 47% Elective, % 53% Demographics Age, mean (SD) 55.9 (8.0) Male, % 81% Race, % Caucasian 89% Black, not of Hispanic origin 5% Other 6% BMI, mean (SD) 30.9 (6.0)
Characteristic Other risk factors, % Current smoker 26% Prior MI 30% Prior PCI 33% Prior CABG 20% Current on dialysis 1% Peripheral vascular disease 9% Diabetes 29% Clinical evaluations, % CAD presentation of STEMI 15% CAD presentation of NSTEMI 7% CAD presentation of unstable angina 24%
Anginal classification within 2 weeks-CCS IV 17%
NYHA Class within 2 weeks-Class IV 10% Cardiomyopathy or LV systolic dysfunction 4% Stress/imaging study - positive results 55%
Statistical Methodology
Modeling
• Variables associated with costs and deemed clinically relevant were considered
• A backward selection algorithm was used to choose the best fit model
• Cross validation was performed to avoid overfitting the data
Cost as a Continuous Outcome
• Generalized linear models with a gamma distribution were used to predict costs
Cost as a Dichotomous Outcome
• Modeled using logistic regression to predict a patient having costs in the top 25 percentile
Predictive Model – Continuous Outcome (n=388)
Cos
ts
Observation
Actual Value
Predicted Value
Variables included Effect direction
Main effectsAgeElective PCI (vs. Emergent PCI)Prior MIPrior PCICAD presentation of STEMICAD presentation of NSTEMIStress/imaging study - positive resultsInteraction TermsAge^2BMI^2Age * BMIAge^2 * BMIAge^2 * BMI^2Age^2 * CAD presentation of NSTEMIPrior MI * Stress/imaging study - positive resultsPrior PCI * CAD presentation of STEMI
Predictive Model – Dichotomous Outcome (n=388)
Model predicting “high costs” as the outcome (top quartile of costs)
Variables Included Effect direction
Independent VariablesAgeBMIElective PCI (vs. Emergent PCI)CAD presentation of STEMICAD presentation of unstable anginaStress/imaging study - positive resultsInteraction TermsAge * BMICAD presentation of unstable angina * Stress/imaging study - positive results
Results Stratified by Elective vs. Non-elective
Emergent PCI (n = 183)
Elective PCI (n = 205)
AUC = 0.71
AUC = 0.72
Variables included in fitted model Effect direction
Independent VariablesAgeBMIStress/imaging study - positive resultsInteraction TermsAge * BMIAge * BMI^2
BMI^3
Variables included Effect direction
Independent VariablesBMIFamily history of premature CADPrior MIBeta blocker use within past 2 weeksInteraction
BMI * Prior MI
Limitations
• Small sample size and limited power in the integrated data, especially with stratifying by PCI status and treating cost as a dichotomous outcome
• Possible incomplete capture of Medicare costs in individuals 65 years and older
• Potential limitations in application to non-commercial and non-Cleveland Clinic patients
• Limited auditing of registry data sources
Challenges - Legal/Compliance
• Non Disclosure Agreement (NDA) • Development of Data Transfer and Collaboration
Agreement (DTCA) • Agreement on specific legal clauses for protecting
patient privacy and business interests for data sources
• Business Associate Agreement (BAA) • Protection of non-CCF provider fee schedules through
the use of imputed costs • Institutional Review Board (IRB) approval
Challenges - Business
Challenges - Technical
Findings
• Clinical data and administrative healthcare data are limited by collection methods and clinical accuracy
• HC/ANTM’s in-depth data quality assessment was valuable when utilizing clinical data for research purposes
• Integrating multiple data sources is complex • Expectation management for timelines and budgets is
essential • Interoperability issues should be considered early in the
project
The Value of Health IT • Episode based treatment can lead to an
increase in care coordination and patient satisfaction
Satisfaction
• Bundled payments promote healthcare system transformation by reducing variations in care
Treatment/Clinical
• LPR efforts provide a better understanding of how patient variables affect outcomes
Electronic Information/Data
• Episode based treatment can promote patient engagement
Prevention/ Patient Education
• Use of integrated data for the PCI research study shows applications for payment innovation
Savings
http://www.himss.org/ValueSuite
Questions? Suma Thomas, MD, MBA, FACC Vice-Chairman, Operations and Strategy Cleveland Clinic , Cardiovascular Medicine Phone: (216) 445-0473 Soyal Momin, MS, MBA Director, Research Environment/Informatics HealthCore/Anthem Inc. Phone: (404) 384-4526