session 6 emerging issues in medical malpractice predictive modeling

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

mailto:[email protected]

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


Exciting Trends in Patient Safety


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


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)


The Actuary’s Opportunity


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.”


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


Goal of Predictive Modeling


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%


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)


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


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


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”


Predictive Modeling Basics


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


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


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)


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


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


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


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.”


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”

session 6 emerging issues in medical malpractice predictive modeling

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