Data Mining: Opportunities for Healthcare Quality Improvement & Cost Control Joseph A. Welfeld,...
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Transcript of Data Mining: Opportunities for Healthcare Quality Improvement & Cost Control Joseph A. Welfeld,...
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 [email protected]@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??