using machine learning to unlock value across the ... · source: cms 2016 us healthcare...
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Using machine learning to unlock value across the healthcare value chain
• ML is a set of techniques that can identify patterns in data to make informed predictions or locate anomalies
• ML can generate value through enhanced physician decision making, reduced waste and fraud, and improved administrative efficiency
• ML is not a magic wand—it needs expert monitoring to maximize model impact
Machine learning (ML) 101
Challenges Potential organizational solutions
Payers and providers need to overcome a few challenges to best leverage ML techniques
Lack of artificial intelligence (AI) strategy
• Investor-like approach to funding AI use cases• Dedicated AI leader with strong Analytics Interpreter
bench
Fragmented data storage and tech limitations
• Shared, inexpensive computing resources • Investment in data infrastructure and storage systems• Strong data governance for quality and traceability
Tight talent market
• Home-grown data science and translator talent• Shared service model across the organization for
AI/ML
Disbelief in AI’s potential
• “Quick-win” use cases leveraging existing datasets
To discover more insights from
McKinsey’s Healthcare Analytics & Delivery, visit
https://healthcare.mckinsey.com/topic/healthcare-analytics
SOURCE: CMS 2016 US healthcare expenditure estimate; proprietary McKinsey models; team analysis
Potential use cases across healthcare
Inform clinical decisions on effective care management and preventive care solutions
At-risk patient prediction
Help primary care physicians identify optimal specialists based on patient needs and preferences
Patient referral
Analyze long-term patient / member journeys through learned prediction of preferences and clinical events (e.g., use individual clinical data to predict preventable catastrophic events)
Improved patient / member retention
Payment services streamlining
Improve claim accuracy and reduce administrative inefficiencies (e.g., during claims submission and processing)
Payers and providers could reduce fraud,
waste, and abuse
(FWA) by $20-30Bn from the
US healthcare value chain by implementing
ML
1 Selected portion of algorithm presented (from perspective of Payer claims editing system)CPT: Current Procedural Terminology (published by the American Medical Association); PTP: Procedure to Procedure; HCPCS: Healthcare Common Procedure Coding System; E&M: Evaluation and Management
Case example: decision model for FWA detection
HCPCS Q0091 (Pap smear)
NO YES
CPT 99395/99396on same claim
CPT 99395/99396on same claim
YES NO YES
Recover
NO
Do not recover … more rulesCPT 36415On the same claim
Do not recover
Is E&M procedure on the same claim
… more rules Global surgery 090 on same day by same provider
NO YES
RecoverDo not recover
Global surgery 090 on same day by same provider
YES
Recover
NO
Do not recover
YES NO NO
NO PTP edit present in claim with no
modifier allowed YES
YES
Modifier 59 Modifier 59
HCPCS G0101on same claimDo not recoverYES NO
NO
Do not recover
RecoverDo not recoverGlobal surgery 090 in next 3
days at same hospitalIs PPO claim
YES NO YES NO
YES
HCPCS G0101on same claim
YES
Recover
NO
Do not recover
Using ML techniques, we designed an algorithm to identify claim recovery cases for unbundling of services – the snapshot below describes an ML algorithm that can be converted into rules to implement in a payer claims editing system1
Supervised learning (e.g., ML classifiers such as neural nets, random forests): Algorithms use training data and feedback from experts to identify relationships from a set of inputs to a given output
Unsupervised learning (e.g., K-means clustering, hierarchical clustering): Algorithms explore input data and identify patterns of behavior. This helps in cohort analysis and anomaly detection
Machine learning techniques
… more rules
YES NO