Data Mining:Opportunities for Healthcare

Quality Improvement & Cost ControlJoseph A. Welfeld, FACHEJoseph A. Welfeld, FACHE

Long Island UniversityLong Island University845.359.7200 x 5410845.359.7200 x 5410Joe.welfeld@liu.eduJoe.welfeld@liu.edu

March 7, 2005March 7, 2005

The Health Information Technology Summit West

Data Mining: Opportunities for Data Mining: Opportunities for Healthcare Quality Improvement & Healthcare Quality Improvement & Cost ControlCost Control

Speaker Profile Data Mining Quality Improvement – Changing

Behavior with Incentives Cost Control – Targeting Key Areas Data Mining Software Practical Applications – A Case Study Questions

Speaker Profile – Joseph A. WelfeldSpeaker Profile – Joseph A. Welfeld Regional Operations Director: NY - RelayHealth Program Director: Graduate Program in Health

Administration: LIU – Rockland Graduate Campus 30 years of healthcare experience CEO - Ocean State Physicians Health Plan Regional VP – United Healthcare 10 years in strategy consulting for IPAs, PHOs &

Hospital Networks MBA Healthcare Administration – CUNY/Mt. Sinai

School of Medicine

Data Mining: DefinitionData Mining: Definition

An information extraction activity whose goal is to discover hidden facts contained in databases.

True data mining software doesn't just change the presentation, but actually discovers previously unknown relationships among the data.

The Healthcare Database MinefieldThe Healthcare Database Minefield Hospital claims data – billing systems Medical claims data – billing systems Pharmacy claims data – PBMs Lab data systems Aggregators:

Managed Care Organizations Third Part Administrators Medical Groups/IPAs None of the above

Data Mining: Data Mining: Obstacles in Healthcare OrganizationsObstacles in Healthcare Organizations

Deer in the headlights look Data what? We don’t have any more money to buy

software We have all the software we need We just spent $__million on a new system Our IT staff can produce anything we want from

our in-house data system Our data analysis could not be better

Quality Improvement – The ChallengeQuality Improvement – The Challenge

Finding acceptable standards Combining data from multiple sources Limited financial incentives to promote

change Until recently, no financial incentives to

change Goal – physician “behavior” change

HEDIS Standards Leapfrog Group Bridges to Excellence MCO Performance Incentives

Quality Improvement – The Quality Improvement – The OpportunitiesOpportunities

Sample HEDIS Report Activity:Sample HEDIS Report Activity:Beta Blocker Treatment After Heart AttackBeta Blocker Treatment After Heart Attack Members age 35 and older who where

discharged with an AMI and were prescribed beta-blockers within 7 days of discharge.

Numerator: Members who received an ambulatory prescription for a beta-blocker within 7 days of discharge

Denominator: Members with an AMI between Jan 1 and Dec 24 of the measurement year

Problem Faced: Linking admission/discharge and prescribing data

Beta Blockers Prescribed after MI Beta Blockers Prescribed after MI Diagnosis: Diagnosis:

ATENOLOL

COREG

INDERAL

LABETOLOL

SOTALOL

BETAPACE

PROPRANOLOL

NORMODYNE

Use of Appropriate Medications:Use of Appropriate Medications:People with AsthmaPeople with Asthma

Numerator: Members age 5-56 who received a prescription for a long term control asthma medication such as inhaled cortico-steroids

Denominator: Members age 5-56 are identified as having asthma using pharmaceuticals and diagnostic data during the year prior to the measurement year Four dispensing events One ER visit with a principle diagnosis of asthma One acute inpatient discharge with a principal diagnosis of

asthma At least four outpatient visits with a diagnosis of asthma

and two dispensing events

Cost Control – The ChallengeCost Control – The Challenge

Payer – Provider “ trust chasm” The “my patients are sicker” debate Combining data from multiple sources

into coherent and logical reports

The ability to merge medical claims, hospital claims, drug claims, medical records and clinical outcomes data

The ability to analyze episodes of care including drug utilization

The ability to rapidly create contract models by user-defined resource and provider categories

Ability to drill down into individual patient claims Ability to target high cost trends

Cost Control – The OpportunitiesCost Control – The Opportunities

Cost Control: Targeting High Cost Cost Control: Targeting High Cost TrendsTrends Puts up to 3 datasets side-by-side. Can compare performance against

benchmarks. Unlimited number of resource categories and

user-defined resource utilization models allowed

Tracks in-patient, professional, lab, pharmacy and other cost categories automatically

See example:

Isolate a resource category and quickly find highest cost by any factor (disease risk group, age, sex, plan, doctor, etc.)

Then drill down to get more information on those results

Drill down further to see treatment line items for those specific patients

Example on following screens shows disease groups with highest lab costs

Cost Control: Drilling Down to Cost Control: Drilling Down to SpecificsSpecifics

ACRG2 Metastatic Category: 5 episodes with very high costsACRG2 Metastatic Category: 5 episodes with very high costs

Those 5 Patient Episodes in the ACRG2 Metastatic Group Those 5 Patient Episodes in the ACRG2 Metastatic Group

Cost Control: Age/Sex AnalysisCost Control: Age/Sex Analysis

Creates unlimited number of age distribution models to apply against data

Select specific resource categories to view Cross-tab against specific values of any

factor, i.e., disease group, specialty, etc. The following slide shows the utilization of

selected resources by Age/Sex for patients in the Asthma-Diabetes-CHF CRG categories:

Cost Control: Physician ProfilingCost Control: Physician Profiling

Functions designed to monitor physician activity Monitor ICD9 and CPT code utilization patterns Cross-tab against specific values of any factor,

i.e., disease group, specialty, etc. Summarizes all costs by provider and compares

on one screen.

ER Utilization Costs by PCP:ER Utilization Costs by PCP:•Outliers shown above dotted line on graphOutliers shown above dotted line on graph•Highest outlier on graph highlighted on chartHighest outlier on graph highlighted on chart

CPT Codes for Gastroenterologists: Ranked by FrequencyCPT Codes for Gastroenterologists: Ranked by Frequency

PCP Utilization Cost Summary by Major Resource CategoryPCP Utilization Cost Summary by Major Resource Category

Detailed 3M CRG (Clinical Risk Groups)Detailed 3M CRG (Clinical Risk Groups)Disease/Severity Cost DistributionDisease/Severity Cost Distribution

Detailed 3M CRG (Clinical Risk Groups) Disease/Severity Cost Detailed 3M CRG (Clinical Risk Groups) Disease/Severity Cost DistributionDistribution

Hudson IPA – A Case StudyHudson IPA – A Case Study Strategic Question – How to deliver real value to

managed care organizations? Replace capitated agreement with performance-based

model Provide managed care organizations data analysis

capabilities they don’t really have Assist with HEDIS performance monitoring and

communications – a key MCO objective

Data Mining Software – Bringing ValueData Mining Software – Bringing ValueGave IPA: Ability to merge medical claims, hospital claims, drug claims, medical

records and clinical outcomes data Ability to analyze episodes of care including drug utilization to meet

agreed-upon goals Ability to rapidly create contract models by user-defined resource and

provider categories Ability to drill down into individual patient claims Ability to analyze HEDIS performance criteria including diabetes and

cardiology care

Powerful disease state management and risk contract functionality

Data warehouse designed to merge all types of healthcare data.

Physician profiling and resource tracking features Drill down into individual patient claims from either

financial or clinical perspectives and retrieve both types of information together

Data Mining Software CharacteristicsData Mining Software Characteristics

Data Mining Software Data Mining Software CharacteristicsCharacteristics

SmartCare – Developed by VantagePoint Health Information Systems, Inc.

Loads claims data at a rate of 100,000 claims/hr

Links pharmacy (PBM), hospital & medical claims

Automatically creates episodes of care Computes PM/PM ratios in less than five

seconds Powerful graphing & statistical tools No programming/data analysis skills/staff

needed Open database for addition of other clinical or

administrative fields – lab, blood pressure, etc.

Data Mining Applications SummaryData Mining Applications SummaryGives Physician Organizations: Ability to develop quality indicators, performance improvement

programs and incentive-based compensation programs. Ability to analyze HEDIS performance criteria including diabetes and

cardiology care. Ability to analyze formulary compliance activity. Tool for additional revenue resources including comprehensive market

research, clinical outcomes and pharmaco-economic studies. Ability to monitor risk-contract progress.

Data Mining Applications SummaryData Mining Applications SummaryCan Give Managed Care Organizations: A tool to develop true partnership

relationships with provider organizations seeking incentive compensation or risk relationships

Ability to develop comprehensive HEDIS analysis and performance reports

Ability to combine multiple claims data bases into a single data reporting and analysis system at the contracting level

Ability to do rapidly model the impact of fee schedule changes on provider costs and contract performance.

Questions??