Data analytic and predictive model for analysis of causes for a loss from diagnosis related groups (DRGs) payment of inpatient at Golden Jubilee Medical Center, Faculty of Medicine Siriraj Hospital, Mahidol University
Keywords:Medical service reimbursement, Diagnostic Related Group (DRGs), Predictive model, Program R
Golden jubilee Medical Center, Faculty of Medicine Siriraj Hospital, Mahidol university is a secondary care level general hospital. Hospital have in-patient service since 2008, most of the patients was reimbursed from the Comptroller General’s Department. Hospital used Diagnostic Related Group Method (DRGs), (DRG Version 6.2.1) to calculate for a reimbursement from Comptroller General’s Department. We observed a loss from DRGs reimbrusememt in some patients (cost from treatment more than money from reimbursement)
From hospital in-patient database, we analyse data since October 2016 to April 2019. Hospital have 6,871 admission number, we observed a loss from DRGs reimbursement in 1,441 admission number or 20.95%, and cost 13,489,521.75 bath.
We analyzed data by ranking a diseases by amount of loss, number of cases and sum of length of stay. We selected 3 diseases within top-10 ranking in each category, we selected 1) Urinary tract infection, site not specific
2) Lobar pneumonia, unspecified) and 3) Cerebral infarction, unspecified to run a predictive model (Decision tree) by program R.
After we run a predictive model, we found; Immobility condition, Length of stay, GI hemorrhage complication, E.coli organism, Age range and Diabetes in co-morbid is a predictive factors for a loss in interested diseases and hospital can bring this data to improve hospiatal process and utilize medical treatment to reduce a loss in the future.