About the Challenge
Congratulations to the Winners of AHRQ's Bringing Predictive Analytics to Healthcare Challenge!
AHRQ scored each challenge entry and selected five (5) winners, who were awarded prize money. The winners are:
HCA Healthcare-NC Division, Asheville, NC--Matthew Lundy (team leader), Andrew Johnson, Kaitlyn Bankieris, Hannah Marshall, and Mabelle Krasne
The HCA Healthcare team used the county-level data provided by AHRQ with additional data from the Area Health Resources Files (AHRF) maintained by the Health Resources and Services Administration (HRSA) and data from the County Health Rankings & Roadmaps Program, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute to estimate their predictive analytic models using R, a software for statistical computing and graphics. After testing multiple combinations of ways to reduce the dimensions of data elements in the model, techniques to impute missing values, and predictive approaches, the team deployed XGBoost model with feedforward imputation for their winning predictions.
Premier, Inc, Charlotte, NC--John Martin (team leader), Michael Duan, Michael Herron, Michael Korvink, and Michael Long
The Premier Healthcare Solutions team used the county-level data provide by AHRQ with additional data from the AHRF maintained by HRSA to estimate their predictive analytic models using R and Python, a programming language. The team used elastic net regularization with cross-validation to narrow the selection of variables to be included in their model. In addition, missing values were imputed using multivariate imputation. The team deployed decision tree regression with adaptive boosting, an AdaBoost model, for their winning predictions.
Analytics Resource Center--Children's Hospital, Aurora, CO--Anusha Guntupalli (team leader), Alex Brown, Brad Ewald, Charles Huhn, Gordon Teubner, Irene Filatov, Jane Bundy, Jennifer Sadlowski, Kaitlin Calhoun, Marisa Payan, Sadaf Samay, and Todd Miller
The Children's Hospital team used the county-level data provided by AHRQ with additional data from the AHRF maintained by HRSA, and data from the US Department of Agriculture, US Census Bureau, US Bureau of Labor Statistics, and Centers for Medicare & Medicaid Services (CMS) to estimate their predictive analytic models using R. After testing several machine learning models, the team deployed the ensemble methods and random forests methods to product the winning predictions.
Kalman & Company, Inc, Virginia Beach, VA--Brian Kadish (team leader), Zach Pryor, Jacob Walzer, Daniel Mask, and Andrew Onufrychuk
The Kalman & Company team used the county-level data provided by AHRQ with additional data from the US Department of Agriculture, US Census Bureau, US Bureau of Labor Statistics, and CMS to estimate their predictive analytic models using Microsoft Excel and Python. The team deployed a variety of methods including exponential triple smoothing with seasonality and random walk, among others, for their winning predictions.
Ursa Health, Nashville, TN--Colin Beam (team leader), Andrew Hackbarth, and Robin Clarke
The Ursa Health team used the county-level data provided by AHRQ with additional data from the Centers for Disease Control and Prevention and the US Department of Housing and Urban Development to estimate their predictive analytic models using R. The team deployed gradient boosting regression to produce their winning predictions.
The Challenge team held an orientation webinar on April 9, 2019, from 4:00-5:30 p.m. ET, to give an overview and respond to questions about the Challenge expectations.
(YouTube Video: 1 hour, 7 minutes, 35 seconds)
*Please note that a portion of the video (approximately 41:00-44:30) has no dialog due to a technical problem with a speaker's audio connection. There is no missing information, just silence during that portion of the webinar before the content resumes.
You may also wish to review the Frequently Asked Questions.
Traditional approaches in health services research rely on rigorous methods, the availability of recent data, and peer review to assure the highest quality of analyses. However, some decision-makers must make policy decisions quickly with the current information available and cannot wait until new analyses are complete. Predictive analytics and related methods may offer a solution to balance the need for rapid information and academic rigor. While predictive analytics and related methods have been used successfully in many fields, their use to understand how healthcare is delivered is largely unexplored.
Bringing Predictive Analytics to Healthcare (2 minutes 1 second)
Audio description version (2 minutes 1 second)
The purpose of the Bringing Predictive Analytics to Healthcare Challenge is to explore how predictive analytics and related methods may be applied and contribute to understanding healthcare issues. AHRQ invites applicants to use predictive analytics and related methods to estimate hospital inpatient utilization for selected counties in the U.S. Building on AHRQ’s current data infrastructure, AHRQ will provide applicants, who have executed the data use agreement required for participation in this Challenge, access to customized analytic files that include information on hospital inpatient discharges for years 2011 to 2016.
The timeline for the Challenge is March 27 to June 28, 2019.
This Challenge encourages participants to apply independently or team with others, including health services and social science researchers, health IT developers, healthcare providers, and others with the appropriate expertise to apply predictive analytics and related methods using AHRQ’s current data infrastructure, as well as any other publicly available data.