title: [Cost analysis of radiotherapy provided in inpatient setting -  testing potential predictors for a new prospective payment system].
abstract: BACKGROUND
  As a part of the development of a new prospective payment model for radiotherapy we analyzed data on costs of care provided by three comprehensive cancer centers in the Czech Republic. Our aim was to find a combination of variables (predictors) which could be used to sort hospitalization cases into groups according to their costs, with each group having the same reimbursement rate. We tested four variables as possible predictors -  number of fractions, stage of disease, radiotherapy technique and diagnostic group.
  
  
  METHODS
  We analyzed 7,440 hospitalization cases treated in three comprehensive cancer centers from 2007 to 2011. We acquired data from the I COP database developed by Institute of Biostatistics and Analyses of Masaryk University in cooperation with oncology centers that contains records from the National Oncological Registry along with data supplied by healthcare providers to insurance companies for the purpose of retrospective reimbursement.
  
  
  RESULTS
  When comparing the four variables mentioned above we found that number of fractions and radiotherapy technique were much stronger predictors than the other two variables. Stage of disease did not prove to be a relevant indicator of cost distinction. There were significant differences in costs among diagnostic groups but these were mostly driven by the technique of radiotherapy and the number of fractions. Within the diagnostic groups, the distribution of costs was too heterogeneous for the purpose of the new payment model.
  
  
  CONCLUSIONS
  The combination of number of fractions and radiotherapy technique appears to be the most appropriate cost predictors to be involved in the prospective payment model proposal. Further analysis is planned to test the predictive value of intention of radiotherapy in order to determine differences in costs between palliative and curative treatment.
authors.forename: J
authors.initials: J
authors.lastname: Sedo
authors.forename: M
authors.initials: M
authors.lastname: Bláha
authors.forename: T
authors.initials: T
authors.lastname: Pavlík
authors.forename: P
authors.initials: P
authors.lastname: Klika
authors.forename: L
authors.initials: L
authors.lastname: Dušek
authors.forename: T
authors.initials: T
authors.lastname: Büchler
authors.forename: J
authors.initials: J
authors.lastname: Abrahámová
authors.forename: V
authors.initials: V
authors.lastname: Srámek
authors.forename: P
authors.initials: P
authors.lastname: Slampa
authors.forename: L
authors.initials: L
authors.lastname: Komínek
authors.forename: P
authors.initials: P
authors.lastname: Pospíšil
authors.forename: O
authors.initials: O
authors.lastname: Sláma
authors.forename: R
authors.initials: R
authors.lastname: Vyzula
mesh_terms: D002173:Cancer Care Facilities / Q000191:economics* / Q000706:statistics & numerical data; D003365:Costs and Cost Analysis; D018153:Czech Republic; D003953:Diagnosis-Related Groups; D019583:Dose Fractionation, Radiation; D006760:Hospitalization / Q000191:economics* / Q000706:statistics & numerical data; D006801:Humans; D009369:Neoplasms / Q000532:radiotherapy*; D011445:Prospective Payment System / Q000191:economics*; D011878:Radiotherapy / Q000191:economics