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Machine Learning in Operational & Administrative Healthcare workflow


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

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Machine Learning in Operational & Administrative Healthcare workflow

  1. 1. Data Science for Healthcare Operations/Administrative April 21, 2017
  2. 2. Page 2 © AlgoAnalytics All rights reserved Outline Machine Learning in Operational & Administrative Healthcare About AlgoAnalytics
  3. 3. Page 3 © AlgoAnalytics All rights reserved 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. 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
  5. 5. Page 5 © AlgoAnalytics All rights reserved Technology:
  6. 6. Page 6 © AlgoAnalytics All rights reserved 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
  7. 7. Page 7 © AlgoAnalytics All rights reserved Machine Learning in Operational & Administrative Healthcare Revenue Cycle Management (RCM) Cash Forecasting Team Resource Allocation Optimization
  8. 8. Page 8 © AlgoAnalytics All rights reserved 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
  9. 9. Page 9 © AlgoAnalytics All rights reserved 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)
  10. 10. Page 10 © AlgoAnalytics All rights reserved 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. 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. 12. Interested? Contact us: April 21, 2017