Varshney, Kush R

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Kosmopolis

Machine Learning from Health Insurance Administrative Data: Opioids, Obamacare, and Other Applications

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11/20/20171IBM Research AI / November 14, 2017 / © 2017 IBM CorporationMachine Learning from Health Insurance Administrative Data: Opioids, Obamacare, and Other ApplicationsKush R. Varshneykrvarshn@us.ibm.comhttp://krvarshney.github.ioMachine Learning on Administrative Data: Lots of Examples I’ve Worked On2IBM Research AI / November 14, 2017 / © 2017 IBM Corporation• HR and compensation data to predict voluntary resignation of IBM employees• Work products data to estimate skills and expertise of IBM employees• Enterprise data to predict successful team compositions of IBM employees• Roll call, bill co-sponsorship, and bill text data to predict voting patterns of members of Congress• Application form and repayment data to predict behaviors of pay-as-you-go solar power customers in rural India• Application form data and evaluation history to predict recipients of a prestigious social entrepreneurship fellowship• Grand slam tennis match statistics data to predict the winner of each point11/20/20172Medical Claims3IBM Research AI / November 14, 2017 / © 2017 IBM CorporationUsed by payers (insurance companies) to reimburse health providers (physicians, hospitals, etc.)Date of serviceDiagnosis codes (ICD-10), procedure codes (CPT), drug codes (NDC)Billed amount and paid amountProvider and patient information (demographics)Useful for many different machine learning tasks• Cost prediction, e.g. Obamacare• Understanding health patterns, e.g. opioid addictionA Tale of Two LawsPATIENT PROTECTION AND AFFORDABLE CARE ACTChanged the landscape of the health insurance market in the United StatesHealth insurance companies had to decide which new markets to enter– New markets defined by geography, age group, and other prospect base criteriaHEALTH INSURANCE PORTABILITY AND ACCOUNTABILITY ACTRequired all releases of health-related information about individuals to protect their privacy– Even for health insurance companies’ internal planning usesk-Anonymity is a common mathematical interpretation of the privacy condition4IBM Research AI / November 14, 2017 / © 2017 IBM Corporation11/20/20173Desiderata of Health Insurance Companies5IBM Research AI / November 14, 2017 / © 2017 IBM CorporationDesire low-cost (healthy) people enroll in their plansNot allowed to accept or deny enrollment on an individual basisAllowed to offer or not offer plans in well-defined markets(Allowed to have marketing strategies)Use data-driven decision making for determining whether or not to offer plans in new marketsDesiderata of Health Insurance Companies6Desire cost data on people who will enroll in new marketsOnly have cost data on people who have enrolled in existing marketsHave demographic data on existing marketHave demographic data on new marketRegression problem with covariate shift– Also need to consider enrollment: three-population shiftIBM Research AI / November 14, 2017 / © 2017 IBM Corporation11/20/20174Demographic and Cost Data AvailabilityExisting Market New MarketEnrolledinsurance company has demographic data and cost datainsurance company has nodemographic data or cost dataEveryone(enrolled and not enrolled)insurance company can get demographic data from public sourcesinsurance company can get demographic data from public sourcesIBM Research AI / November 14, 2017 / © 2017 IBM CorporationThe Market Risk Assessment Regression Problem8Use cost and demographic data for enrolled members in the existing market, demographic data for the existing market, and demographic data for the new market to estimate cost for enrolled members in the new marketCan use regression technique of choice– Ordinary least-squares with and without log-transformed data– Two-part models– Generalized linear models– Multiplicative regressionNeed a type of covariate shift to account for the differences between the features of the existing and new marketFor the three population shift and regression workload, need a privacy transformation that preserves the probability distribution of the dataIBM Research AI / November 14, 2017 / © 2017 IBM Corporation11/20/20175Results9Developed such an approach, which required a new privacy-preservation methodExcellent empirical performanceSuccessfully used by a large health insurance companyD. Wei, K. N. Ramamurthy, and K. R. Varshney, “Health Insurance Market Risk Assessment: Covariate Shift and k-Anonymity” SIAM International Conference on Data Mining, pp. 226-234, April-May 2015. Best Research Paper Honorable MentionIBM Research AI / November 14, 2017 / © 2017 IBM CorporationOpioid Epidemic10175 deaths per day due to opioid overdose in the United States Physicians could be indirectly contributing to the epidemic by overprescriptionOne reason is because they have generally lacked data to guide opioid prescribing decisionsUse medical claims data to explore the causal relationships between the characteristics of the initial opioid prescriptions and outcomesIBM Research AI / November 14, 2017 / © 2017 IBM Corporation11/20/20176Approach11IBM Research AI / November 14, 2017 / © 2017 IBM CorporationResults12In patients who are given synthetic opioids for no more than 7 days, using natural or semi-synthetic opioids instead could potentially reduce the risk of long-term use or addiction by 36.5%.For natural or semi-synthetic opioids, a shorter days of supply could potentially reduce the risk by 65.3% in patients with longer days of supply.Longer days of supply on the initial opioid prescription is a driving force of long-term useA significant difference in average treatment effect between synthetic opioids and natural or semi-synthetic onesJ. Zhang, V. S. Iyengar, D. Wei, B. Vinzamuri, H. Bastani, A. R. Macalalad, A. E. Fischer, G. Yuen-Reed, A. Mojsilović, and K. R. Varshney, “Exploring the Causal Relationships between Initial Opioid Prescriptions and Outcomes,” AMIA Workshop on Data Mining for Medical Informatics, November 2017.IBM Research AI / November 14, 2017 / © 2017 IBM Corporation11/20/2017713IBM Research AI / November 14, 2017 / © 2017 IBM Corporation

https://repository.upenn.edu/cgi/viewcontent.cgi?article=1006&amp;context=admindata_conferences_presentations_2017

Machine Learning from Health Insurance Administrative Data: Opioids, Obamacare, and Other Applications

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