Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: May 5, 2019
Open Peer Review Period: May 8, 2019 - Jul 3, 2019
Date Accepted: Aug 9, 2019
(closed for review but you can still tweet)
Geospatial-Temporal, Explanatory, and Predictive Models for Hospital-Based Outpatient Back Surgery
ABSTRACT
Background:
Outpatient back surgery in the United States increased 60% from January 2012 through December 2017, yet the supply of neurosurgeons remained almost constant. During this time, adult obesity grew 5%. An obvious question is the relationship between the two, particularly when considering supply and demand.
Objective:
This research evaluates the demand and associated costs for hospital outpatient back surgery by geo-location over time to evaluate provider practice variation. The study then leverages hierarchical time series to generate tight demand forecasts on an unobserved test set. Finally, explanatory financial, technical / workload, geographical, and temporal factors as well as state-level obesity rates are investigated as predictors for the demand for hospital-based outpatient back surgery.
Methods:
Hospital data from January 2012 through December 2017 were used to generate geospatial, temporal maps and a video of CPT 63* claims. Hierarchical time series modeling provided forecasts for each state, the Census regions, and the nation for an unobserved test set and then again for the outyears of 2018 and 2019. Stepwise regression, lasso regression, ridge regression, elastic net, and gradient-boosted random forests were built on a training set and evaluated on a test set to evaluate variables important to explaining the demand for outpatient-based back surgery.
Results:
Widespread, unexplained practice variation over time is seen on the GIS multimedia mapping. Hierarchical time series provided accurate forecasts on a blind data set and suggest 6.5% growth of hospital-based outpatient back surgery in 2018 and 13% by 2019. The increase in payments by 2019 are estimated to be $323.9 million. Extreme gradient-boosted random forests beat constrained and unconstrained regression models on a 20% unobserved test set and suggested that obesity is one of the most important factors in explaining the increase in demand for hospital-based outpatient back surgery.
Conclusions:
Practice variation and obesity are factors to consider when estimating demand for hospital outpatient back surgery. Federal, state, and local planners should evaluate demand-side and supply-side interventions for this emerging problem. Administrators may use methods promulgated in this research to predict and anticipate growth in demand for specific services and respond through hiring action, agile shifting of internal workload, or external-contract activities for services outside the organization’s capacity.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.