DeepAI AI Chat
Log In Sign Up

Early Prediction of Post-acute Care Discharge Disposition Using Predictive Analytics: Preponing Prior Health Insurance Authorization Thus Reducing the Inpatient Length of Stay

by   Avishek Choudhury, et al.

Objective: A patient medical insurance coverage plays an essential role in determining the post-acute care (PAC) discharge disposition. The prior health insurance authorization process postpones the PAC discharge disposition, increases the inpatient length of stay, and effects patient health. Our study implements predictive analytics for the early prediction of the PAC discharge disposition to reduce the deferments caused by prior health insurance authorization, the inpatient length of stay and inpatient stay expenses. Methodology: We conducted a group discussion involving 25 patient care facilitators (PCFs) and two registered nurses (RNs) and retrieved 1600 patient data records from the initial nursing assessment and discharge notes to conduct a retrospective analysis of PAC discharge dispositions using predictive analytics. Results: The chi-squared automatic interaction detector (CHAID) algorithm enabled the early prediction of the PAC discharge disposition, accelerated the prior health insurance process, decreased the inpatient length of stay by an average of 22.22 for state government hospitals, 2,346 for non-profit hospitals and 1,798 for for-profit hospitals per day. The CHAID algorithm produced an overall accuracy of 84.16 value of 0.81. Conclusion: The early prediction of PAC discharge dispositions can condense the PAC deferment caused by the prior health insurance authorization process and simultaneously minimize the inpatient length of stay and related expenses incurred by the hospital.


page 10

page 11

page 12


Characterizing the Value of Information in Medical Notes

Machine learning models depend on the quality of input data. As electron...

Do Hospital Data Breaches Reduce Patient Care Quality?

Objective: To estimate the relationship between a hospital data breach a...

Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series

Given a cardiac-arrest patient being monitored in the ICU (intensive car...

Improving Early Sepsis Prediction with Multi Modal Learning

Sepsis is a life-threatening disease with high morbidity, mortality and ...

Complexity Analysis of Approaching Clinical Psychiatry with Predictive Analytics and Neural Networks

As the emerging field of predictive analytics in psychiatry generated an...