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


• 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


Four types

of analytics


Five types

of analytics


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


Artificial Intelligence: Tasks ML forms the basis for AI, which is

ML at scale.


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


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


Rosemary Hayes

([email protected])


Value Based Care

Rosemary Hayes

([email protected])

Payer

Pharma

Member

CMSProvider

Lifestyle

Family


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


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


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


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


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.)


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

http://www.jneurosci.org/content/37/32/7595


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.

http://auapps.american.edu/alberto/www/analytics/ISLRLectures.html

Documents

Machine Learning and AI for Payer and Provider Analytics