Ben Webster’s Data Modeling Technique for Predicting Hospital Stays is Published
The data science team at NLP Logix is proud to announce and congratulate Ben Webster for having the work he did in applying advanced statistical models to big data sets published in the renowned research publication, The Journal of Basic and Applied Research International. The work has led to a predictive model that can provide an accurate range for how long a patient will be hospitalized for a diagnosis recognized by the Affordable Care Act as an increased driver of costs.
“I’d first like to thank Dr. Pali Sen of the University of North Florida for being the Corresponding Author for this work,” said Ben Webster. “It has become a passion of mine to solve problems by using big data sets and applying advanced statistical models to reveal new insights that can be acted upon.”
To support his work, Ben acquired publically available data from the Florida Agency for Health Care Administration (AHCA)for the year 2011. This data, which is void of personally identifiable information, contained 2,655,588 records of detailed information for every inpatient encounter for every hospital in the state of Florida. He then focused on the variables surrounding three diagnosis, Acute Myocardial Infarction (AMI), Heart Failure (HF) and Pneumonia (PN) and began applying advanced statistical analysis to identify trends that would impact a patient’s given length of stay.
The result of Ben’s work will not just remain in the publication, but will shortly be available for hospitals and others interested in predicting the average length of stay a patient will encounter, through a site on the NLP Logix corporate web site. “We specialize in not only developing highly accurate predictive models, but equally important, we have the tools and years of experience to deliver the results to the customer at the point where decisions are being made,” said Matt Berseth, Lead Data Scientist at NLP Logix.
“I believed that I could help Florida hospitals get insights into the relationship between condition, severity, and length of stay for the first three diagnoses that the Affordable Care Act focused on, to work as a foundation to investigate excessive utilization and readmission. Having this work published reinforces that belief.”