Daniel Otero-León

Daniel Otero-León

PhD IOE Candidate

University of Michigan

Biography

My research interests are generally in the area of operations research and, more specifically, in the area of stochastic models and stochastic dynamic programming with applications to service systems, including health systems and revenue management. My dissertation research is in the area of data-driven models for improving decision-making in the context of cardiovascular disease, with the help of clinical collaborators at the U.S. Department of Veteran Affairs. I am currently analyzing longitudinal electronic medical record data and pharmacy claims data to optimize appointment follow-up policies for patients. My goal is to reduce the risk of cardiovascular disease.

I am an active member of the Institute for Operations Research and the Management Sciences (INFORMS), and I currently serve as the President of the INFORMS student chapter at the University of Michigan.

Academic advisors:

Brian T. Denton, Ph.D. and Mariel S. Lavieri, Ph.D.

Interests

  • Markov Decision Processes
  • Stochastic Optimization
  • Healthcare
  • Revenue Management

Education

  • MSc in Industrial Engineering, 2012

    Universidad de los Andes, Colombia

  • BSc in Industrial Engineering with Mathematics Minor, 2010

    Universidad de los Andes, Colombia

Publications

Using Longitudinal Health Records to Simulate the Impact of National Treatment Guidelines for Cardiovascular Disease (Accepted)

Continuous tracking of patient’s health data through electronic health records (EHRs) has created an opportunity to predict healthcare policies’ long-term impacts. Despite the advances in EHRs, data may be missing or sparsely collected. In this article, we use EHR data to develop a simulation model to test multiple treatment guidelines for cardiovascular disease (CVD) prevention. We use our model to estimate treatment benefits in terms of CVD risk reduction and treatment harms due to side effects, based on when and how much medication the patients are exposed to over time. Our methodology consists of using the EM algorithm to fit sparse health data and a discrete-time Monte-Carlo simulation model to test guidelines for different patient demographics. Our results suggest that, among published guidelines, those that focus on reducing CVD risk are able to reduce treatment without increasing the risk of severe health outcomes.

Working Paper: Population based cholesterol follow-up policy

Preventing chronic diseases is an essential aspect of medical care for healthy patients, but deciding when to collect information, such as the patient’s cholesterol levels, is difficult. Measuring too frequently may be unnecessary and costly; on the other hand, measuring too infrequently means the patient may forgo needed treatment and experience adverse events related to the disease. We present results from estimating a stochastic model based on longitudinal data for cholesterol in a large cohort of patients seen in the national Veterans Affairs health system. We further use this model to study policies for when to collect measurements to assess the need for cholesterol lowering medications.

Optimal timing of airline promotions under dilution (2019)

Offering promotions has become common practice in the airline industry as a strategy to boost the total revenue. An effective promotion campaign should be adequately priced and timed to attract sufficient extra demand and compensate for the markdown price. Diversion of demand from the regular fare to the markdown price is also a side-effect of offering promotions, which needs to be considered in designing successful campaigns. Demand dilution occurs when customers are attracted to the promotional fare from higher fare families, or from future purchases to the promotional time window. We propose a stochastic dynamic model for the optimal timing of promotions, considering both types of dilution and given fixed prices for the regular and promotional fares. We prove the existence of an optimal policy, and derive structural properties to find the minimum number of unsold seats that justifies the promotion under dilution. We examine the performance of this model on two cases from a Latin American airline and demonstrate considerable savings by applying our proposed optimal policy versus the airline’s current policy.

A stochastic dynamic pricing model for the multiclass problems in the airline industry (2015)

In the airline industry, deciding the ticket price for each flight directly affects the number of people that in the future will try to buy a ticket. Depending on the willingness-to-pay of the customers the flight might take off with empty seats or seats sold at a lower price. Therefore, based on the behavior of the customers, a price must be fixed for each type of product in each period. We propose a stochastic dynamic pricing model to solve this problem, applying phase type distributions and renewal processes to model the inter-arrival time between two customers that book a ticket and the probability that a customer buys a ticket. We test this model in a real-world case where as a result the revenue is increased on average by 31 percent.

Talks

Talk: Patient Prioritization Model With Limited Resources And Stochastic Compliance.

Physicians seek to prevent chronic diseases by tracking the patients’ healthcare behavior. They handle a large heterogeneous panel of …
Talk: Patient Prioritization Model With Limited Resources And Stochastic Compliance.

Talk: Using Longitudinal Health Records to Simulate the Impact of National Treatment Guidelines for Cardiovascular Disease.

Continuous tracking of patient’s health data through electronic health records (EHRs) has created an opportunity to predict the impact …
Talk: Using Longitudinal Health Records to Simulate the Impact of National Treatment Guidelines for Cardiovascular Disease.

Talk: Dynamic Updating For Prediction Models For Medication Adherence.

Adherence to medication is a significant factor when preventing chronic diseases. When patients adhere poorly, physicians intervene to …
Talk: Dynamic Updating For Prediction Models For Medication Adherence.

Seminar: Stochastic Dynamic Programming: Applications in the Airline Industry and Healthcare Sector.

Stochastic Dynamic Programming (SDP) is an optimization methodology that considers the variability of the system. SDP is studied …

Seminar: Stochastic Dynamic Programming: Applications in the Airline Industry and Healthcare Sector.

Poster: Effects of follow-up policies on statin adherence.

Adherence to medication is a significant factor when dealing with the prevention of cardiovascular diseases. Medications can reduce the …
Poster: Effects of follow-up policies on statin adherence.

Teaching Experience

 
 
 
 
 

Instructor on record

IOE 316: Introduction to Markov Processes

Aug 2021 – Dec 2021 University of Michigan, Ann Arbor, MI, USA
Topics Taught: Stochastic Modeling, Exponential distribution, Poisson Process, Markov Chains, and Dynamic Programming.
 
 
 
 
 

Lecturer

IIND 3107: Marketing Engineering

Jan 2017 – May 2018 Universidad de los Andes, Bogota, Colombia
Topics Taught: Marketing Basics, Segmentation, Forecasting, Pricing, Social Network Marketing, New Products Strategy.
 
 
 
 
 

Lecturer

IIND 3113: Discrete Event Simulation

Jun 2014 – Dec 2017 Universidad de los Andes, Bogota, Colombia
Topics Taught: Input Modeling, Random Variable Generation Models, Validation and Verification, Experimental Design, Variance Reduction Approaches, and Simulation with SIMIO.
 
 
 
 
 

Lecturer

IIND 2109: Decision Analysis Tools

Aug 2013 – Dec 2016 Universidad de los Andes, Bogota, Colombia
Topics Taught: Excel, Visual Basic, Access, and SQL.
 
 
 
 
 

Lecturer

IIND 2104: Stochastic Modeling

Aug 2012 – Dec 2021 Universidad de los Andes, Bogota, Colombia
Topics Taught: Stochastic Modeling, Exponential distribution, Poisson Process, Markov Chains, and Dynamic Programming.

Contact

  • 1891 IOE Building 1205 Beal Avenue, Ann Arbor, MI 48109
  • DM Me