Accession Number : ADA324187
Title : Identification of Variables Predictive of Payment in Full of Third Party Outpatient Claims.
Descriptive Note : Final rept. Jul 95-May 96,
Corporate Author : ACADEMY OF HEALTH SCIENCES (ARMY) FORT SAM HOUSTON TX HEALTH CARE ADMINISTRATI ON
Personal Author(s) : Moore, Leslie A.
PDF Url : ADA324187
Report Date : MAY 1996
Pagination or Media Count : 49
Abstract : The purpose of this study was to determine, using multiple discriminant analysis, the effects of the predictor variables, CPT (grouped to make visit type) codes, specific third party payers, and the number of claims, on payment in full of third party outpatient billings at Naval Medical Center San Diego, for fiscal year 1994. Two random samples were extracted from the Third Party Collection database. One sample (N=147) consisted of those bills which were paid in full; the other (N=150) was made up of those bills which were not paid in full. Discriminant function analysis was used to distinguish among the groups, based on the predictor variables. Stepwise multiple regression was then employed to determine the contribution of the variables to payment in full. Results of the study indicate that the third party payer is a significant predictor of payment in full. However, nearly 77 percent of the claims not paid in full are due to deductibles which have not been met and require copayments; both are situations over which military treatment facilities have no control. The main implication of this study is that particular third party payers are more likely than others to pay a claim in full. The relationships with these payers should be cultivated in an attempt to recoup as much outpatient visit charges as possible. All facility staff coming into contact with patients must maintain a conscientious effort to identify patients with third party payers. Further, the staff must ensure maximum compliance with the Third Party Program initiatives in order to collect whenever the opportunity is present.
Descriptors : *ACCOUNTING, *PATIENTS, *COLLECTION, DATA BASES, MILITARY FACILITIES, PREDICTIONS, THESES, VARIABLES, REGRESSION ANALYSIS, SAMPLING, FUNCTIONAL ANALYSIS, OUTPATIENT CLINICS, DISCRIMINATE ANALYSIS.
Subject Categories : Economics and Cost Analysis
Medicine and Medical Research
Distribution Statement : APPROVED FOR PUBLIC RELEASE