3750 ldr 102813 webcast3 · 5 presented by: pamela peele, phd, is the chief analytics officer of...
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Presented by:
Pamela Peele, PhD, is the chief analytics officer of the UPMC Insurance Services Division. Peele brings 13 years of patient care experience along with 12 years of academic research experience to her position as the leader of healthcare analytics at the Health Plan. She is responsible for data analytic activities, economic modeling, predictive modeling, and statistical analysis. Her work focuses on the application of economic and statistical models to improve the health and welfare of UPMC members. She currently holds faculty appointments in Health Policy & Management and in Psychiatry in the School of Medicine at the University of Pittsburgh. She is core faculty at the Center for Research on Health Care at the University of Pittsburgh Medical Center and an elected fellow of the Centre for Interuniversity Research and Analysis on Organizations in Montreal, Canada.
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Presented by:
Christine VanZandbergen, MPH, MS, PA-C,is the associate clinical information officer at
Penn Medicine, a $4.3 billion healthcare provider organization consisting of over 2,000 physicians providing services to the Hospital of the University of Pennsylvania, Penn Presbyterian Medical Center, Pennsylvania Hospital and the health system network that serves the city of Philadelphia, the surrounding five-county area and parts of southern New Jersey. She joined Penn Medicine in June 2002 as a Cardiac Surgery Physician Assistant and is currently responsible for oversight of all clinical decision support tools from the ideation process, through pilot, and deployment across the health system in a multi-system EHR. She continues her clinical practice in cardiac surgery at Pennsylvania Hospital.
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BUILDING A LEARNING ORGANIZATION FOR POPULATION HEALTH MANAGEMENT
Pamela Peele, PhD
Chief Analytics Officer
UPMC Insurance Services Division
August 2013
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Levels of Analytics Framework
Standard ReportsWhat happened?
AlertsWhat actions are needed?
Query DrilldownWhat exactly is the problem?
Ad hoc ReportsHow many, how often, where?
Statistical AnalysisWhy is this happening?
OptimizationWhat’s the best that can happen?
Predictive ModelingWhat will happen next?
ForecastingWhat if these trends continue?
Degree of Intelligence
Com
petit
ive
Adva
ntag
e
From Tom Farre, “The Analytical Competitor”, in Analytics: The Art and Science of Better, ComputerWorld Technology Briefing.
UPMC HP: 2009
UPMC HP: 2006
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Staff – 2006
Business Analyst (30)
Accounting
• Excel
• Access
• Crystal Reports
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Current Staff
Clinical Program
Evaluation (5)
Epidemiology
Biostatistics
Health Services Research
Strategic Business Analysis
(6)
Finance
Economics
Policy
Statistics
Database & Data
Quality (7)
Finance
Economics
Policy
Statistics
Modeling (3)
Physics
Mathematics
Biomedical Engineering
Statistics
Operations (5)
Economics
Industrial Engineering Operations
Communications
Statistics
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Staff Skills and Backgrounds
• Industry Knowledge
• Data visualization skills
• Data ECTL (extraction, cleaning, transformation, loading) skills
• Statistics
• Health Services Research
• Data Mining
• Financial modeling & evaluation
• Presentation, writing, and communication skills
• Formally trained but NOT blinded by their training
– Challenge deeply held beliefs
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Tools
• Database: SQL, Toad
• Statistics: SAS, STATISTICA, STATA, R
• Data Mining: STATISTICA, R
• Text Mining: STATISTICA
• Modeling & Simulation: MATLAB, Mathematica, Vensim, GEPHI
• GIS: ArcGIS
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Integrated Data to Support Clinical Management Population Health Strategy and Clinical Support
• Many types of disparate data available
• Medical Claims
• Behavioral Health Claims
• Pharmacy Claims (allows medication possession ratio MPR)
• Workers’ Compensation Claims
• Short-Term Disability
• Absenteeism Data From Time Cards
• On-Site Biometric Screening Results
• Health Risk Assessments – (self-reported)
• Care Management Assessments/Phone interaction
• Enrollment & Demographic Data
• Lab Values
GOAL: develop centralized registry of member clinical presentation and lifestyle profiles for clinical analysis
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Identifying Health Conditions by SEPARATE Data Source
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1,596 1,994 2,197 2,344
4,086 5,698 5,698 6,774
4,324 6,588 6,588 7,658982 2,715 2,715 2,715
2,200 6,366 7,597 7,597
2,738 2,738 2,738 2,738
0 1,442 5,721 6,119
132 132 8,593 8,878
11,795 16,036 21,005 21,913
Identifying Health Conditions by AGGREGATING Data Source
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Stratification Data Flow
HealthPlaNET
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PREDICTIVE ANALYTICS AND READMISSION RISK
Using the electronic health record to automatically identify patients at risk for 30-day readmission
Christine VanZandbergen, MPH, PA-C
Associate Clinical Information Officer
Penn Medicine
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The University of Pennsylvania Health System
• Four acute care hospitals:
– Hospital of the University of Pennsylvania
– Penn Presbyterian Medical Center
– Pennsylvania Hospital
– The Chester County Hospital
– 1,740 acute care beds
– 80,020 acute care admissions in 2012
– 73 ACGME-accredited training programs
Transforming Data Into Value
Phase 3 – Precision MedicineData
Inform
ation
Knowledge
Phase 2 – “Meaningful Use”
Phase 1 – Foundation
YEAR
VALU
E
2004 2006 2008 2010 2012 2014 2016 2018 2020
• Implement Common Inpatient EMR Applications
• Implement Common Ambulatory Applications
• Support Standard Processes Across Penn Medicine
• Aggregate Patient Data in Centralized Data Warehouse
• Development of Dashboards and Alerts
• Integration With Perelman School of Medicine IS
• Development of Early Warning Surveillance System
• High Performance Computing Center
• Research Data Warehouse
• Implement Integrated EMR
• Integrate Phenotype and Genomic Data
• Establish Enterprisewide IS Governance Structure
• Solidify Enterprise IS Infrastructure
• Extend Mobility Capabilities
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Key Elements: Operational Predictive Analytics
Operational ReadinessOperational Readiness
Analytics CapabilityAnalytics Capability
Data Capture
Data Capture
• Advocacy• C-suite• Front line
• Infrastructure
• Advocacy• C-suite• Front line
• Infrastructure
• Predictable• Versatile• Predictable• Versatile
• Accurate• Reliable• Available
• Accurate• Reliable• Available
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Penn Strategy
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Background: National Readmissions
Source: “After Hospitalization: A Dartmouth Atlas Report on Post-Acute Care for Medicare beneficiaries” September 2011.
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Purpose
• To develop simple predictive model for 30-day readmission
• Implement an automated alert in our hospital’s electronic health record that identifies on admission patients at high risk for 30-day readmission
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Methods: Systematic Review
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Method: Predictors From Review
• Healthcare resource utilization
– Length of stay, number of prior admissions, and previous ED visits
– Studies have not consistently identified threshold values for these predictors
• Patient characteristics
– Comorbidities, living alone, discharged to home, and payer
– Evidence is mixed regarding other factors, including age and gender
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Analysis: Prediction Rule
Rule Sensitivity PPV % of Total Hospital
Visits Flagged
Looking 12 months back from Index HospitalizationPH>0 and PE>0 30 14 11
PH> 1 25 19 7
PH>1 and PE>0 20 19 5
PH>1 or PE>1 33 13 13
Looking six months back from Index Hospitalization
PH>0 36 14 13
PH>1 18 24 4
PH = prior hospitalization PE = prior ED visit
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Analysis: Final Prediction Rule
Rule Sensitivity PPV % of Total Hospital
Visits Flagged
Looking 12 months back from Index HospitalizationPH>0 and PE>0 30 14 11
PH> 1 25 19 7
PH>1 and PE>0 20 19 5
PH>1 or PE>1 33 13 13
Looking six months back from Index Hospitalization
PH>0 36 14 13
PH>1 18 24 4
Patients with >1 inpatient admission in the 12 months preceding the current inpatient stay
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Implementation:Readmission Risk Flag
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Implementation:Readmission Risk Flag
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Results: 21 months After implementation
42% 85%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Readmission No Readmission
First 21 Months After Implementation
Readmit Flag
No Flag
Sensitivity Specificity
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22% 93%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Readmission No Readmission
First 21 Months after Implementation
Readmit Flag
No Flag
PPV NPV
Results: 21 months After implementation
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Readmission Rates Entity One
-1%
1%
3%
5%
7%
9%
11%
13%
15%
2010
-07
2010
-08
2010
-09
2010
-10
2010
-11
2010
-12
2011
-01
2011
-02
2011
-03
2011
-04
2011
-05
2011
-06
2011
-07
2011
-08
2011
-09
2011
-10
2011
-11
2011
-12
2012
-01
2012
-02
2012
-03
2012
-04
2012
-05
2012
-06
2012
-07
2012
-08
2012
-09
2012
-10
2012
-11
2012
-12
2013
-01
2013
-02
2013
-03
2013
-04
2013
-05
2013
-06
% E
nco
un
ters
UPHS 7 & 30 day unscheduled readmission, FY11 - FY13
30 Day 07 Day
Implementation
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Readmission Rates Entity One
-1%
1%
3%
5%
7%
9%
11%
13%
15%
2010
-07
2010
-08
2010
-09
2010
-10
2010
-11
2010
-12
2011
-01
2011
-02
2011
-03
2011
-04
2011
-05
2011
-06
2011
-07
2011
-08
2011
-09
2011
-10
2011
-11
2011
-12
2012
-01
2012
-02
2012
-03
2012
-04
2012
-05
2012
-06
2012
-07
2012
-08
2012
-09
2012
-10
2012
-11
2012
-12
2013
-01
2013
-02
2013
-03
2013
-04
2013
-05
2013
-06
% E
nco
un
ters
Entity 1: 7 & 30 day unscheduled readmission, FY11 -FY13
30 Day 07 Day
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-1%
1%
3%
5%
7%
9%
11%
13%
15%
2010
-07
2010
-08
2010
-09
2010
-10
2010
-11
2010
-12
2011
-01
2011
-02
2011
-03
2011
-04
2011
-05
2011
-06
2011
-07
2011
-08
2011
-09
2011
-10
2011
-11
2011
-12
2012
-01
2012
-02
2012
-03
2012
-04
2012
-05
2012
-06
2012
-07
2012
-08
2012
-09
2012
-10
2012
-11
2012
-12
2013
-01
2013
-02
2013
-03
2013
-04
2013
-05
2013
-06
% E
nco
un
ters
Entity 2: 7 & 30 day unscheduled readmission, FY11 -FY13
30 Day 07 Day
Readmission Rates Entity Two
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Readmission Rates Entity Three
-1%
1%
3%
5%
7%
9%
11%
13%
15%
2010
-07
2010
-08
2010
-09
2010
-10
2010
-11
2010
-12
2011
-01
2011
-02
2011
-03
2011
-04
2011
-05
2011
-06
2011
-07
2011
-08
2011
-09
2011
-10
2011
-11
2011
-12
2012
-01
2012
-02
2012
-03
2012
-04
2012
-05
2012
-06
2012
-07
2012
-08
2012
-09
2012
-10
2012
-11
2012
-12
2013
-01
2013
-02
2013
-03
2013
-04
2013
-05
2013
-06
% E
nco
un
ters
Entity 3: 7 & 30 day unscheduled readmission, FY11 -FY13
30 Day 07 Day
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Implementation
5%
6%
7%
8%
9%
10%
11%
12%
13%
14%
15%
2010
-07
2010
-08
2010
-09
2010
-10
2010
-11
2010
-12
2011
-01
2011
-02
2011
-03
2011
-04
2011
-05
2011
-06
2011
-07
2011
-08
2011
-09
2011
-10
2011
-11
2011
-12
2012
-01
2012
-02
2012
-03
2012
-04
2012
-05
2012
-06
2012
-07
2012
-08
2012
-09
2012
-10
2012
-11
2012
-12
2013
-01
2013
-02
2013
-03
2013
-04
2013
-05
2013
-06
HUP PAH PMC
UPHS Entities: 30 Day Unscheduled Readmissions
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30-Day Readmission Rates
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30-Day Readmission Rates
0%
5%
10%
15%
20%
25%
30%
En
cou
nte
rs (
%)
UPHS Readmission rate per Risk Flag group
Risk Flag Group: 30 Day Readmission Rate No Risk Flag Group: 30 Day Readmission Rate
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Conclusion
• Proof of concept
• Real-time, “actionable” data
• Integrates into provider workflow
• Available to all care providers
• Unclear impact on outcomes
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Limitations andFuture Directions
• Needs further prospective evaluation
• Improve sensitivity and specificity
• How is the information used?
• Impact on clinical care and outcomes
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Future Directions:Predictive Analytics
Data Warehouse
Data Warehouse
Operational System
Operational System
Data Mining
Predictive
Model
Predictive
Model
Implementation
Information
Implementation
Information
EvaluationEvaluation
Operational System
Operational System
Predictive Goal
Predictive Goal
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BUILDING A LEARNING ORGANIZATION FOR POPULATION HEALTH MANAGEMENT
Pamela Peele, PhD
Chief Analytics Officer
UPMC Insurance Services Division
August 2013
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CMS Penalties
Fiscal Year 2013 2014 2015
Targeted Conditions AMI, HF, PN AMI, HF, PN AMI, HF, PN, COPD, CABG, PCI, VascularProcedures
Maximum Penalty 1% 2% 3%
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CMS adjusts for age, sex, comorbid diseases, and past medical history. Each hospital is evaluated with respect to the national average.
All-cause 30-day readmits are also included as a HEDIS quality measure in CMS’ star program for health plans
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Geographic Variationin Readmit Rates
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Modeling Issues
• Data is usually “local”
• Data is usually a “snapshot”
• Data is fine-grained
– 13,000 ICD-9 codes
• Will increase to 68,000 with ICD-10
– Example ICD-10: V9107XA - Burn due to water-skis on fire
• Can roll-up using Clinical Classification Software (CCS)
– 9,600 CPT4 codes
• Can roll-up using Berenson Eggers Type of Service
• Target (30-day readmit) is relatively small
• Readmission rates are complicated by the presence of comorbidities
• Most models do not focus on actionability46
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Days to Readmit as aProxy for Actionability
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Example: 10% of all Anemia discharges are readmitted within 10 days
4848
6: Operations on the respiratory system7: Operations on the cardiovascular system16: Miscellaneous diagnostic and therapeutic procedures
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Readmission Models
• Most models focus on:
– A specific population: Medicare, Medicaid, etc.
– A specific disease: Heart Failure, COPD, …
– A single hospital
• 18% of all 30-day readmits occur at a different hospital
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Identification of At-Risk Patients
Allaudeen, Schnipper, et. al. “Inability of Providers to Predict Unplanned Readmissions,” J Gen Intern Med., 2011 Jul.
• 159 discharges
• Age ≥ 65
• Evaluated readmission predictions from: Attending Physician, Resident Physician, Intern Physician, Case Manager, Nurse
– None of the predictions differed from chance
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Readmission Models
Kansagara, Englander, et. al. “Risk Prediction Models for Hospital Readmission: A Systematic Review,” JAMA, 2011 Oct.
• Reviewed 26 models
– “Most current readmission risk models perform poorly”
• Table 4 contains complete list of predictor variables for all used in at least one model
– Most common:
• Specific medical diagnosis or CCI (24)
• Age (19)
• Sex (15)
• Prior admissions (14)
• Alcohol or substance use (11)
• Mental health comorbidity (9)
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Model Types
• Models derived from retrospective administrative data
– May be used for comparison of hospitals
• Models derived from real-time data
– EMR
– Augmenting administrative data with clinical data does not meaningfully improve an HF readmission model: Hammill, Curtis et. al., 2011
• Models derived from survey or chart review data
• Almost all models are based upon a logit model
– Exceptions: Neumann “Measuring Performance in Health Care: …”, 2004; Amalakuhan et. al. “A Prediction Model for COPD Readmissions…,” 2011; Lee, “Selecting the Best Prediction Model for Readmission,” 2012.
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Notable Models
Hasan, Meltzer, et. al., “Hospital Readmission in General Medicine Patients: A Prediction Model,” 2009 Dec. (online); J Gen Intern Med., 2010 Mar.
• General Population
• c-statistic = 0.61
• Insurance status
• Marital status
• Has regular physician
• CCI
• SF12 health questionnaire
• Number of prior admissions
• Length of stay
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Notable Models
van Walraven, Dhalla, et. al. “Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community,” Canadian Medical Association Journal, 2010 Apr.
• General Population
• c-statistic = 0.684
Length of stay
Acuity (emergent admission)
Comorbidity (CCI)
Emergency department visits
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Notable Models
Amarasingham, Moore, et al., “An Automated Model to Identify Heart Failure Patients at Risk for 30-Day Readmission or Death Using Electronic Medical Record Data,” Med Care, 2010 Nov.
• Tabak mortality score
• History of depression/anxiety
• Single
• Male
• Number of address changes
• Medicare patient
• In lowest socioeconomic quintile
• History of cocaine use
• History of missed clinic visits
• Number of prior admissions
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Notable Models
Donzé, Aujesky, et. al, “Potentially Avoidable 30-Day Hospital Readmissions in Medical Patients,” JAMA Intern Med., 2013 Apr.
• Target = Avoidable Readmission (36.6% of all readmissions)
• c-statistic = 0.71
Hemoglobin at discharge
Oncology
Sodium Level at discharge
Procedure during index admission
Index Type of admission (elective vs. non-elective)
Admissions during previous 12 months
Length of stay56
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El Camino Hospital Case Study
• Predictors of readmission:
– Age
– Discharge location
– Is the patient’s PCP identified in the medical record?
– Diagnosis for
• CHF
• PN
• Stroke
• Sepsis
• Renal failure
• Resulted in 25% reduction in readmits
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http://www.cio-chime.org/chime/press/CaseStudy/ElCamino_Case_Study.pdf
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Allina Health Model
• General Population
• c-statistic = 0.73
• 30 Variables
– Clinical Data
– Demographic Data
– LOS
– Prior Admits
.
.
.
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http://www.sourcemediaconferences.com/HCS13/Hauptshare.pdf
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UPMC Readmission Model
• 1.5 years of discharges to home
– 50,600 in training set
– 21,700 in validation set
• Claims-based model
• Boosted Decision Tree
– Predictions are generated prior to the index admission
– Predictive Positive Value = 28%
• Base Readmit Rate = 14.8%
– Sensitivity = 57%
• Model captures over half of all readmissions
– AUC = 0.6859
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0.990.880.770.660.550.440.330.22
500
400
300
200
100
0
Probability
Fre
qu
en
cy
0.70.5
Distribution of Probability for ReadmissionFY09 Acute Inpatient Discharges (All LOB)
n = 38,840
Most impactable opportunity to
prevent readmission
Most impactable opportunity to
prevent readmission
Discharge Advocate: Risk Models Identify Readmission “Sweet Spot”
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At discharge, patients lacked competency in their own conditions and care: • 37% able to state the purpose of all their medications• 14% knew their medication’s common side effects• 42% able to state their diagnosis
Low Risk of ReadmissionLow Risk of
ReadmissionLess impactable despite
high readmission riskLess impactable despite
high readmission risk
Single Acute Episodes
Early/Mid Stage Chronic Disease
End StageChronic Disease
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UPMC Model Performance
Condition Sensitivity Specificity
DM with complications 74.6% 60.2%
Sickle Cell 74.4% 53.8%
Hypovolemia 71.6% 70.5%
Complications of surgical or medical care
65.2% 63.2%
Respiratory infection 59.2% 73.1%
Obstructive chronic bronchitis 69.7% 59.8%
Asthma 59.3% 74.2%
Spondylosis 24.0% 91.0%
Osteoarthritis 16.2% 93.8%
All 54.5% 74.4%
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Sensitivity by WeekPost Discharge
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Sensitivity increases after two weeks
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Days to Readmit as Proxyfor “Actionability”
MDC Description Readmit Rate
Days to Readmit –10th Percentile
1 Diseases of the Nervous System 12.9% 20*
4 Diseases of the Respiratory System 15.7% 17
5 Diseases of the Circulatory System 15.2% 16
6 Diseases of the Digestive System 14.2% 17
7 Diseases of the Hepatobiliary System & Pancreas 18.2% 12
10 Endocrine, Nutritional & Metabolic Diseases 11.7% 24
11 Diseases of the Kidney & Urinary Tract 17.0% 14
16 Diseases of the Blood & Blood-forming Organs & Immunological Disorders
27.3% 9
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* 10% of all MDC 1 discharges are readmitted within 20 daysResults based on 211,198 discharges to home
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Effect of Planned Visiton Readmits
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Questions & Answers
Christine VanZandbergen, MPH, MS, PA-CAssociate Clinical Information Officer
Penn MedicinePhiladelphia, Pa.
Pamela Peele, PhDChief Analytics Officer
UPMC Health PlanPittsburgh, Pa.
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“Pre
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Anal
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s:
Redu
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