1.
PREDICTIVE ANALYTICS IN
HEALTH INSURANCE
Prasad Narasimhan – Technical Architect

2.
PREDICTIVE ANALYTICS ?
• Predictive analytics is the practice of extracting information from existing
data sets in order to determine patterns and predict future outcomes and
trends. It does not tell you what will happen in the future but forecasts what
might happen in the future with an acceptable level of reliability, and
includes what-if scenarios and risk assessment.

3.
PREDICTIVE ANALYTICS & BIG DATA
• Predictive analytics is an enabler of big data: Businesses collect vast amounts
of real-time customer data and predictive analytics uses this historical data,
combined with customer insight, to predict future events. Predictive analytics
enable organizations to use big data (both stored and real-time) to move
from a historical view to a forward-looking perspective of the customer.

4.
HEALTHCARE ANALYTICS MARKET
• Real time analytics is carried out on the spot and helps in quick decision
making, for instance, clinical decision support software with active
knowledge systems use two or more items of patient data to generate case-
specific advice.
• Batch Analytics retrospectively evaluates past data such as patient records
and claims data from an insured population, which helps in predictive
modeling and cost control measures.

5.
PROCESS INVOLVED
Project Definition / Business Understanding
Exploration / Data Understanding
Data Preparation
Model Building
Deployment

6.
REQUIREMENT :
• Predictive analytics service providers generally start by studying the
characteristics of people who have already purchased a product from an
insurer,
• and then develop a profile — or model — of the kind of person who buys
that specific insurance product.

7.
VARIABLES IN PREDICTIVE ANALYTICS
ALGORITHM
• Predicting which policy holders (or potential policy holders) will make a
claim
• And how long it will be until they make the claim.
• The more data available on the history of claims
• And ‘extraneous’ information about the policy holder

8.
DATA

9.
CASE STUDY
In this case analysis of hospital data was done to
optimize and balance human resources, medication
and time spent on each patient to improve clinical
outcomes. Fig.1 performs spectral partitioning of
the graph that was built using the data from the
health-care agency. Understanding the structure of
the data and capturing hidden interrelationships
helped to improve the existing resource allocation
schema. As a result created a model of resource
harness that stopped overspending and improved
the quality of patient's care.

10.
Contd……….
In this case analysis of patient’s symptoms was taken to predict
the development of the disease. Fig.2 demonstrates principal
component analysis and support vector machine classifier.
Healthcare data analytics allows us to find patterns that help to
recognize early stages of the disease and predict its
development. This predictive model provides the hospital with an
opportunity to control the occurrence of epidemics as well as be
more accurate in early diagnosis of the disease.

11.
MODEL EVALUATION
• Entries will be judged by comparing
• The predicted number of days a member will spend in the hospital with the actual number
of days a member spent in the hospital in DaysInHospital_Y4 (not given to competitors)
• Prediction accuracy will be evaluated based on the following metric
• The objective function for the model to minimize
where
1. i is a member;
2. n is the total number of members;
3. pred is the predicted number of days spent in hospital for member i in the test period;
4. act is the actual number of days spent in hospital for member i in the test period.

12.
IMPROVING HEALTH CARE DELIVERY
• To identify when patients are likely to have a hospital stay
• And to direct health care providers to take preventative actions to avoid the
hospital stay.
• Prediction of product demand,
• Options prices,
• Turnover likelihood of sales leads.

13.
APPLICATION
 Model drug development collaborations that maximize IP and drug
discovery.
 Simulate PRO (Patient Reported Outcomes) for care quality
improvement and outcomes.
 Accelerate time to market for new therapies with strategic portfolio
modeling
 Predict market access and optimize resource allocation for new
therapies
 Predict high risk patients for ACO (accountable care organization)
and hospitals.
 Leverage advanced analytics to reduce hospital readmissions
 Simulate connected health consumer and recommend technology
interventions that drive healthy behavior change.
 Simulate the financial risks and incentives of emerging
reimbursement models for ACO.