Machine learning in the operations and administration of healthcare workflows provides for an effective RCM & productivity in Team allocation

1. Data Science for Healthcare
Operations/Administrative
April 21, 2017

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Outline
Machine Learning in
Operational &
Administrative Healthcare
About AlgoAnalytics

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CEO and Company Profile
Aniruddha Pant
CEO and Founder of AlgoAnalytics
PhD, Control systems, University of
California at Berkeley, USA 2001
• 20+ years in application of advanced mathematical techniques
to academic and enterprise problems.
• Experience in application of machine learning to various
business problems.
• Experience in financial markets trading; Indian as well as global
markets.
Highlights
• Experience in cross-domain application of basic scientific
process.
• Research in areas ranging from biology to financial markets to
military applications.
• Close collaboration with premier educational institutes in India,
USA & Europe.
• Active involvement in startup ecosystem in India.
Expertise
• Vice President, Capital Metrics and Risk Solutions
• Head of Analytics Competency Center, Persistent Systems
• Scientist and Group Leader, Tata Consultancy Services
Prior Experience
• Work at the intersection of mathematics and other
domains
• Harness data to provide insight and solutions to our
clients
Analytics Consultancy
• +30 data scientists with experience in mathematics
and engineering
• Team strengths include ability to deal with
structured/ unstructured data, classical ML as well as
deep learning using cutting edge methodologies
Led by Aniruddha Pant
• Develop advanced mathematical models or solutions
for a wide range of industries:
• Financial services, Retail, economics, healthcare,
BFSI, telecom, …
Expertise in Mathematics and Computer
Science
• Work closely with domain experts – either from the
clients side or our own – to effectively model the
problem to be solved
Working with Domain Specialists
About AlgoAnalytics

4. AlgoAnalytics - One Stop AI Shop
Healthcare
•Medical Image
•Work flow optimization
•Cash flow forecasting
Financial Services
•Dormancy prediction
•Recommender system
•RM risk analysis
•News summarization
Retail
•Churn analysis
•RecSys
•Image recognition
•Generating image description
Others
•Algorithmic trading strategies
•Risk sensing – network theory
•Network failure model
•Clickstream analytics
•News/ social media analytics
Aniruddha Pant
CEO and Founder of AlgoAnalytics
•We use structured data to
design our predictive analytics
solutions like churn,
recommender sys
•We use techniques like
clustering, Recurrent Neural
Networks,
Structured
data
•We use text data analytics for
designing solutions like
sentiment analysis, news
summarization and many more
•We use techniques like natural
language processing, word2vec,
deep learning, TF-IDF
Text data
•Image data is used for predicting
existence of particular
pathology, image recognition
and many others
•We use techniques like deep
learning – convolutional neural
network, artificial neural
networks and technologies like
TensorFlow
Image
data
•We use sound data to design
factory solutions like air leakage
detection, identification of
empty and loaded strokes from
press data, engine-compressor
fault detection
•We use techniques like deep
learning
Sound
Data

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Technology:

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Medical diagnostics – Detect serious
disorders or diseases through image
analytics and medico-genomic data
Reducing Readmissions – Use
scoring techniques to locate high
risk customers and prevent
readmissions through higher
monitoring
Population Health Management -
Vast amount of past patient
data/genomics data utilized for
diagnosing high risk group through
risk stratification score
Cash Flow Forecasting – Forecasting
of cash flows based on claims history,
potential denials to forecast cash and
reimbursement analysis
Billing Errors– Identify opportunities
to collect missing income, including
rejected claims by payers or overdue
money from patients
Patient Selection - Insurance
coverage eligibility, identifying
patients who are not likely to
pay in full
Work Flow Optimization– Using
historical data for staffing to reduce
costs, having the right clinician at right
time right place
Efficient Use of Hospital
Resources – Prevent
bottlenecks in urgent care by
analyzing patient flow during
peak times
Fraud and abuse prevention - Cost
trending-forecasting, care utilization
analysis, actuarial & financial analysis
-common applications for preventing
frauds
Trend Analysis - Tracking hospital’s market position and
competitive trends. Visualizing changes in market dynamics
and growth patterns
Grant problem - Predict the likelihood that a particular
proposal will receive a grant using text analytics
Patient Aid
PatientAid
Revenue
Cycle
Management
Resource
Allocation
Otherissues
in
Healthcare
Machine Learning for Healthcare

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Machine Learning in Operational & Administrative Healthcare
Revenue Cycle Management (RCM)
Cash Forecasting
Team Resource Allocation Optimization

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Machine Learning Analytics in Revenue Cycle Management
 Revenue Cycle Management: the financial
process used to track patient care episodes from
registration and appointment scheduling to the
final payment
 An error when filing claims be it a patient’s
name to a wrongly added code, can cause delays
and denial
Patient scheduling &
Demographic
verification
Billing & Coding Claims Submission Denial Management
Machine learning analytics can be leveraged to catch
these mistakes and get them corrected automatically
reversing claims that have been denied inappropriately
Results:
 Improves on verification of patient demographic details
Allows the team to improve on utilization/non utilization of certain codes and pinpoint missing information in
physician documentation
 Improves the denials process for better appeals and preventing future denials of similar nature
 Enhances prediction of patient payment

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Healthcare Revenue Intelligence – Cash Forecasting Model
Read data
Dates, cash, revenue, KPI
values, target values
Lag the data
Lag the data by n days,
Forecast for n days then
Regression Model
Predict the cash values for n
days ahead using data
remaining after taking lag
Claims denials can be better forecasted by using the information available with regard to past denials
Utilizing Machine Learning techniques one can develop a Cash Forecasting Model to help stabilize the RCM process
Goal:
1. Project N-day ahead cash using revenue and important KPIs
2. Project target cash if all KPIs are at target level
3. Find revenue leakage: (N-day Target cash – N-day Forecasted cash)

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Machine Learning: Healthcare Team Allocation Optimization
Three Level Optimization Problem
To allocate resources in a more productive manner that can ultimately help meet patient needs
Team Level
Optimization
• Determine teams best using how team performed in past n days
• Objective : Maximise Total Amount Resolved
• Optimized values : Average Amount Worked, Productivity, Effectiveness Ratio
User Level
Optimization
• Determine teams best using how user and team performs in past n days
• Objective : Maximise User Level Amount Resolved
• Optimized values : Average Amount Worked, Productivity, Effectiveness Ratio
Specific
Recommend
ation
• Study the external actionable influencers affecting Productivity and ER combined
• Give actionable recommendation to achieve desirable Productivity and ER
• Influencers divided in to two subtypes : Private and Government
Each level can be used on it’s own as a
separate recommendation system !!

11. Page 11
Total 200
accounts to
work on a
given day.
100 Pvt.
100 Govt.
Total 7 users
available for
that day to do
a job
Run 3rd level
optimization
for the
available users
Find the work
allocation
percentage
from this
optimization
Divide Pvt. and
Govt. accounts
in the
proportion
determined
User
Govt.
Accounts
Pvt.
Accounts
Total
Accounts
21842 7 10 17
21843 0 37 37
21844 9 17 26
21845 8 7 15
21852 35 9 44
21853 39 11 50
21867 2 9 11
Total 100 100 200
 Input : Available users and number of
Pvt. and Govt. accounts
 Output : Accounts allocation
Machine Learning in Healthcare: Daily Work Allocation
Three level Optimization- Daily allocation of health workers put to work for the private sector payers or government-
aided payers to be able to meet targets and maximize revenues makes for an effective & efficient RCM process,

12. Interested?
Contact us: info@algoanalytics.com
April 21, 2017