Machine Learning and AI for Payer and Provider Analytics
Transcript of Machine Learning and AI for Payer and Provider Analytics
© 2017 Edifecs | PROPRIETARY Page 1
Machine Learning and AI for
Payer and Provider
AnalyticsTechnology and Application Overview
MHDC CIO Forum – May 2018
Dr. Prasad Saripalli, VP Data Science, Edifecs
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• Broad agreement in the industry – ML & AI will significantly alter
and improve healthcare.
• Gap between the generic optimism for revolutionary AI
applications in the distant future such as cyborg physicians, fully
automated clinics and care supported by robotics, and the
current, near-term feasibility of ML and AI use cases from both
business and tech points of view.
• Deconstruct this schism using a few key use cases from the point
of view of 4 stake holders - Payer, Provider, Employer (or State,
CMS) and Consumer (aka Member or patient).
• Show how ML and AI can address such “low hanging fruit” today.
ML and AI for health plans
and providers
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Four types
of analytics
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Five types
of analytics
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Artificial Intelligence “AI is the science and engineering of
making intelligent machines which can
perform tasks that require intelligence
when performed by humans …”
• Tasks that require AI:• Solving a differential equation
• Brain surgery
• Inventing stuff
• Playing Jeopardy
• Playing Wheel of Fortune
• Walking
• Driving
• Grabbing stuff
• Pulling hand away from fire
• Emotion
• Art
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Artificial Intelligence: Tasks ML forms the basis for AI, which is
ML at scale.
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Wicked Problems and Social
Messes: Categories of MessKurtz, CF and Snowden, DJ (IBM Systems Journal 43, 3 Mar 2003)
Category Qualities
I Solution knowledge exists in your domain
II Solution knowledge in another domain
III No solution exists. Complex, but responds consistently to same
stimuli
IV (Wicked) No solution exist. Chaotic and adaptive
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Characteristics of Wicked
Problems
The problem is difficult to define
Multi-causal…may itself contain problems
No rules or markers for where to stop
Each wicked problem is essentially unique
Attempts to address may open cause unforeseen
consequences
No opportunity for trial and error learning with immunity
The planner is held accountable
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Value Based Care
Rosemary Hayes
Payer
Pharma
Member
CMSProvider
Lifestyle
Family
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Machine Learning
Machine Learning - Machine Learns via Data Frames
What Types of Questions Can Data Science Answer?
Machine Learning Pipeline
A Tour of Machine Learning Algorithms
Algorithms: How they Work
1. KNN
2. K-Means Clustering
3. Association Rules (A priori)
4. Outlier Detection
5. Decision Trees
6. Recommender System
7. Text Mining
8. Natural Language Processing
Big Data and Unstructured Data
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Machine Learning
For each type of analysis we consider:
• What problem does it solve, and for whom?
• How is it being solved today?
• How can it beneficially affect business?
• What are the data inputs and where do they come from?
•
• What are the outputs and how are they consumed-
(online algorithm, a static report, etc.)
• Is this a revenue leakage ("saves us money") or a
revenue growth ("makes us money") problem?
Descriptive
Analyses
Prescriptive
Analyses
Predictive
Analyses
Diagnostic
Analyses
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The Contact Lens Data –
Classification Problem
NoneReducedYesHypermetropePre-presbyopic
NoneNormalYesHypermetropePre-presbyopic
NoneReducedNoMyopePresbyopic
NoneNormalNoMyopePresbyopic
NoneReducedYesMyopePresbyopic
HardNormalYesMyopePresbyopic
NoneReducedNoHypermetropePresbyopic
SoftNormalNoHypermetropePresbyopic
NoneReducedYesHypermetropePresbyopic
NoneNormalYesHypermetropePresbyopic
SoftNormalNoHypermetropePre-presbyopic
NoneReducedNoHypermetropePre-presbyopic
HardNormalYesMyopePre-presbyopic
NoneReducedYesMyopePre-presbyopic
SoftNormalNoMyopePre-presbyopic
NoneReducedNoMyopePre-presbyopic
hardNormalYesHypermetropeYoung
NoneReducedYesHypermetropeYoung
SoftNormalNoHypermetropeYoung
NoneReducedNoHypermetropeYoung
HardNormalYesMyopeYoung
NoneReducedYesMyopeYoung
SoftNormalNoMyopeYoung
NoneReducedNoMyopeYoung
Recommended lensesTear production rateAstigmatismSpectacle prescriptionAge
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The Contact Lens Data –
Classification Problem
Decision tree ID3 Algorithm
If tear production rate = reduced then recommendation = none
If age = young and astigmatic = noand tear production rate = normal then recommendation = soft
If age = pre-presbyopic and astigmatic = noand tear production rate = normal then recommendation = soft
If age = presbyopic and spectacle prescription = myopeand astigmatic = no then recommendation = none
If spectacle prescription = hypermetrope and astigmatic = noand tear production rate = normal then recommendation = soft
If spectacle prescription = myope and astigmatic = yesand tear production rate = normal then recommendation = hard
If age young and astigmatic = yes and tear production rate = normal then recommendation = hard
If age = pre-presbyopicand spectacle prescription = hypermetropeand astigmatic = yes then recommendation = none
If age = presbyopic and spectacle prescription = hypermetropeand astigmatic = yes then recommendation = none
Classification Rules
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Machine Learning Process -
Applied to Any Given Use
Case
1. Study the domain
2. Craft Use Cases
3. Identify Questions
4. Build Object Models
5. Build data sets
1. 1 Table per Object
2. Denormalize the data to one master table
3. This is your data frame
Sub-setting the master data set by object
Ask questions about each object
Example: ER Admissions – why does my Plan see so many?
Member; Plan; Provider and Conditions (Traffic, Weather etc.)
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Machine Learning
• Use of synthetic data
• Real world data are very
sparse
• Big Data
• Too many attributes – curse of
dimensionality
• PCA (Principal Components
Analysis)
• Unstructured data
• Conversion
• Dummy Coding
Principle components analysis. A, Two-dimensional plots of the first three principal
components (accounting for 91% of the total variance) relative to one another
reveal that the population data effectively and separately encodes each tastant in
coding space. In all graphs, the individual cell positions are plotted and color coded
according to cluster to demonstrate how each cluster contributes to the coding of
each taste stimulus
Max L. Fletcher et. al. (2017) Overlapping Representation of Primary Tastes in a Defined Region of the
Gustatory Cortex http://www.jneurosci.org/content/37/32/7595
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Algorithms – How They Work
• KNN
• K-Means Clustering
• Association Rules (A priori)
• Outlier Detection
• Decision Trees
• Recommender System
• Text Mining & NLP
• Neural Networks
• Deep Learning
• AI
http://auapps.american.edu/alberto/www/analytics/ISLRLectures.html
We will use R and Labs from the ISLR text book for the hands on part of
the workshop.