session 6 emerging issues in medical malpractice predictive modeling kevin m. bingham – deloitte....
TRANSCRIPT
Session 6
Emerging Issues In Medical Malpractice
PREDICTIVEMODELING
Kevin M. Bingham – [email protected]
Casualty Actuarial Society Annual MeetingTuesday, November 16, 200412:30 PM – 2:00 PMMontreal, Canada
Copyright © 2004 Deloitte Development LLC. All Rights Reserved
INTRODUCTION
• Florida Medical Malpractice Report° www.fldfs.com/companies/pdf/
Med_Mal_2004_Rpt.pdf
• Exciting Trends in Patient Safety• The Actuary’s Opportunity• Goal of Predictive Modeling• Predictive Modeling Basics• Closing Thoughts
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Exciting Trends in Patient Safety
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Patient Safety Organizations and Other Sources• National Patient Safety Foundation (www.npsf.org)
• JCAHO Environment of Care (www.jcaho.org)° Environment of Care° National Patient Safety Goals (2005 goals now available)° Root Cause Analysis
• Medical associations (e.g., American Medical Association - www.ama-assn.org)
• The Leapfrog Group (www.leapfroggroup.org)
• State patient safety organizations (e.g., Virginia - www.vipcs.org)
• Advancements in computerized physician order entry (CPOE) systems
• Safety books (e.g., “The Satisfied Patient” – James W. Saxton)
• Legislative action
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Safety and Errors
• Patient Safety – The prevention of healthcare errors, and the elimination or mitigation of patient injury caused by healthcare errors.
• Healthcare Error – An unintended outcome caused by a defect in the delivery of care to a patient. Healthcare errors may be errors of:° Commission (doing the wrong thing);
° Omission (not doing the right thing); or
° Execution (doing the right thing incorrectly).
Definitions from the National Patient Safety Foundation (www.npsf.org)
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The Actuary’s Opportunity
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Medical Malpractice and the Actuary – Current Role• Traditional Roles
° Pricing° Reserving ° Tort Reform
• The Actuarial Profession’s Challenge° Overcoming the negative perception in the media:
“Actuaries focus on quantifying the price to charge a physician, or the amount of damages that must ultimately be paid to a victim, instead of focusing our energy on preventing injuries in the first place.”
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Medical Malpractice and the Actuary – Future Role?• Shift our Focus Towards the Positive Side of the
Medical Malpractice Equation° Increase our involvement in patient safety initiatives° Increase our eminence on the positive side of the
healthcare equation• Join CPOE efforts• Join PSOs• Submit articles with a heavier focus on patient safety
° Use Predictive Modeling in the U/W process in order to price policies in a manner that promotes patient safety and risk management goals
Definitions from www.npsf.org
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Goal of Predictive Modeling
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Current Perception of Specialty Segmentation
Loss Ratio
Below average
Average
Above average
135%125%
110%115%
100% 90%
80%70%
140%
Internal data
63%60%
65%68%
72%
78%75%
85%
112%
93%
Obstetricians
ChiropractorsOverallL.R. 75%
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Current Perception of Specialty Segmentation
80%70%63%
60%
65%
Loss Ratio - Chiropractors
“All chiropractors are good risks”
Low frequencyLow severityLow profile (i.e., not making headlines)
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Current Perception of Segmentation
135%125%
140%85%
112%
93%
Loss Ratio - Obstetricians
“All OB/GYNs are bad risks”
High frequencyHigh severityHigh profile• Dramatic rate increases• Leaving state• Retiring from practice• Cutting back on services
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Segmentation of the Future
82
66
58
62
70
74
78
90
135
OverallL.R. 75%
40
Dr. Bob Lesse - Chiropractor
Dr. Linda Moore - Chiropractor
Pre
dic
ted
L
oss
Rat
io
Internal / External DataPredicted Loss Ratio
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Segmentation of the Future -The Goal of Predictive Modeling
80%70%63%
60%
65%58
90
Dr. Linda Moore
Dr. Bob Lesse
“Some chiropractors are good risks, some are bad. Focus U/W dollars on good risks.”
Chiropractors
“All chiropractors are good risks”
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Predictive Modeling Basics
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Predictive Modeling Approach
Business Rules EngineBusiness Rules EngineBusiness Rules EngineBusiness Rules Engine
External Data
Internal Data
Synthetic Variables
Data SourcesData Sources
Score For Each PolicyScore For Each Policy
You learn why
Score For Each PolicyScore For Each Policy
You learn why
Build And Test The ModelBuild And Test The Model
Data Aggregation&
Data Cleansing
Evaluate and Create Variables
Develop Loss Predictive Model
Build And Test The ModelBuild And Test The Model
Data Aggregation&
Data Cleansing
Evaluate and Create Variables
Develop Loss Predictive Model
Score Driven Business Applications
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Potential Medical Malpractice Sources
Customer DataCustomer Data
• Area of Specialty• Location of Practice• Correspondence• Policy Records• Billing/Payment History• Medical Practice Information• Risk Management Practices• Professional Publications• Practice Bio/Demo Graphics• Practice Satisfaction Surveys
Customer DataCustomer Data
• Area of Specialty• Location of Practice• Correspondence• Policy Records• Billing/Payment History• Medical Practice Information• Risk Management Practices• Professional Publications• Practice Bio/Demo Graphics• Practice Satisfaction Surveys
Agency InformationAgency Information
• Retention• Recruiting• Profitability• Audited Premium Ratio• New Business Volume
Agency InformationAgency Information
• Retention• Recruiting• Profitability• Audited Premium Ratio• New Business Volume
Claims DataClaims Data
• Losses• Experience Data• Frequency• Timing/Patterns• Loss Control Data• Fraud/Lawsuit
Claims DataClaims Data
• Losses• Experience Data• Frequency• Timing/Patterns• Loss Control Data• Fraud/Lawsuit
3rd Party Database3rd Party Database
• Motor Vehicle Reports• Credit Reports• Experian / Dun & Bradstreet• Enhanced Census / Behavioral• NPDB – Detail/State data• AMA Physician Master File• Florida Closed Claim Database• Geographic / Demographic• Consumer / Behavioral / Lifestyle• Aggregated Pharmacy Data
3rd Party Database3rd Party Database
• Motor Vehicle Reports• Credit Reports• Experian / Dun & Bradstreet• Enhanced Census / Behavioral• NPDB – Detail/State data• AMA Physician Master File• Florida Closed Claim Database• Geographic / Demographic• Consumer / Behavioral / Lifestyle• Aggregated Pharmacy Data
Traditional SourcesPotential Sources
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Data Preprocessing & Modeling
• Data Evaluation° Data Collection/Cleansing° Loss Development° Manual Premium Development° On Leveling° Variable Creation° Univariate Analysis° Data Quality Analysis, Capping,
Binning° Correlation Analysis° Training vs. Testing Data° External Data Matching and
Related Reports
• Modeling Approach° Loss Ratio Transformations° Principal Component Analyses° Stepwise Regression, Forward,
Backward° Generalized Linear Modeling° Neural Network Applications° CART, MARS Algorithms° Comprehensive Actuarial Review
& Analysis° Lift Disruption° Longitudinal Drift° Stability Analysis° Reason Code Distribution° Distribution Analysis (premium,
class group, geographic, cross line)
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Sample Representation of Model
A(PracticeYrs)B + C(CreditScore)D +E(NumPatients)F
+G(NumEmployees)H +I(AdverseActions)J
586586
~40~40--50 Variables50 Variables
Score Expected Loss Ratio=
Weights/Coefficients
Examples
• Years in Practice
• Credit Score
• Monthly # Patients
• Number of Employees
• # Adverse Actions per Zip/County
• Avg Claim Amount per Zip/County
• Avg Prescription Count per Patient
• Others
Examples
• Years in Practice
• Credit Score
• Monthly # Patients
• Number of Employees
• # Adverse Actions per Zip/County
• Avg Claim Amount per Zip/County
• Avg Prescription Count per Patient
• Others
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Predictive Modeling Results inImproved Class Segmentation
BusinessSegmentation
Obstetrics 12% 32% 56%
Dermatology 35% 46% 19%
Internal Medicine 26% 40% 34%
There is profitable business in “under performing” classes
There is unprofitable business in “over performing” classes
The models help to identify both situations
Better Average Poor
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Reason Codes
• Reason Codes identify several traditional / acceptable reasons that will be used for external communications.
• Reason codes explain 80% to 90% of the resulting policy actions
• Reason codes hopefully drive change in attitude towards risk management and patient safety
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Closing Thoughts
• Physician owned organizations might not be receptive to Predictive Modeling° Comfort level regarding personal data (e.g., credit
scoring)° More focused pricing will certainly increase rates
significantly for some physicians (lowering rates for others)
• Patient safety – “It’s time for actuaries to begin focusing more of our efforts on preventing injuries in the first place.”
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Closing Thoughts
• Kaiser Family Foundation Media Advisory for November 17, 2004
“NEW SURVEY ASSESSES PUBLIC'S VIEWS ON HEALTH CARE QUALITY FIVE YEARS AFTER LANDMARK REPORT ON MEDICAL ERRORS”