title: Adjusting case mix payment amounts for inaccurately reported comorbidity data.
abstract: Case mix methods such as diagnosis related groups have become a basis of payment for inpatient hospitalizations in many countries. Specifying cost weight values for case mix system payment has important consequences; recent evidence suggests case mix cost weight inaccuracies influence the supply of some hospital-based services. To begin to address the question of case mix cost weight accuracy, this paper is motivated by the objective of improving the accuracy of cost weight values due to inaccurate or incomplete comorbidity data. The methods are suitable to case mix methods that incorporate disease severity or comorbidity adjustments. The methods are based on the availability of detailed clinical and cost information linked at the patient level and leverage recent results from clinical data audits. A Bayesian framework is used to synthesize clinical data audit information regarding misclassification probabilities into cost weight value calculations. The models are implemented through Markov chain Monte Carlo methods. An example used to demonstrate the methods finds that inaccurate comorbidity data affects cost weight values by biasing cost weight values (and payments) downward. The implications for hospital payments are discussed and the generalizability of the approach is explored.
authors.affiliation: The Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth College, 35 Centerra Parkway, Suite 110, Lebanon, NH 03766, USA. Jason.Sutherland@Dartmouth.edu
authors.forename: Jason M
authors.initials: JM
authors.lastname: Sutherland
authors.forename: Jeremy
authors.initials: J
authors.lastname: Hamm
authors.forename: Jeff
authors.initials: J
authors.lastname: Hatcher
mesh_terms: D015897:Comorbidity; D003953:Diagnosis-Related Groups / Q000145:classification / Q000191:economics*; D006801:Humans; D017059:Models, Econometric; D009010:Monte Carlo Method; D009864:Ontario; D012051:Reimbursement Mechanisms / Q000191:economics
references.citation: Health Aff (Millwood). 2008 Nov-Dec;27(6):1479-90
references.pmid: 18997203
references.citation: J Gerontol A Biol Sci Med Sci. 2007 Mar;62(3):281-5
references.pmid: 17389725
references.citation: Health Aff (Millwood). 2007 Jan-Feb;26(1):124-36
references.pmid: 17211021
references.citation: N Engl J Med. 2006 Nov 16;355(20):2061-4
references.pmid: 17108339
references.citation: Health Aff (Millwood). 2005 Jul-Dec;Suppl Web Exclusives:W5-376-84
references.pmid: 16091408
references.citation: Health Policy. 2007 May;81(2-3):289-99
references.pmid: 16908086
references.citation: Health Policy. 2009 Aug;91(3):321-6
references.pmid: 19250696
references.citation: Health Care Financ Rev. 1982 Dec;4(2):101-28
references.pmid: 10309909
references.citation: Med Care. 1998 Jun;36(6):915-27
references.pmid: 9630132
references.citation: Health Serv Res. 2004 Aug;39(4 Pt 1):1027-45
references.pmid: 15230940
references.citation: Health Care Financ Rev. 2006 Spring;27(3):111-22
references.pmid: 17290652
references.citation: J Health Econ. 2005 Jan;24(1):189-210
references.pmid: 15617794
references.citation: Health Care Manag Sci. 2006 Aug;9(3):211-3
references.pmid: 17016926
references.citation: Eur J Health Econ. 2004 Jun;5(2):95-109
references.pmid: 15452744
references.citation: J Health Econ. 2001 Jul;20(4):461-94
references.pmid: 11469231
references.citation: J Health Econ. 2004 Mar;23(2):369-89
references.pmid: 15019762
references.citation: Health Care Financ Rev. 1988 Winter;10(2):37-46
references.pmid: 10313085
references.citation: Health Policy. 2003 Aug;65(2):101-7
references.pmid: 12849909
references.citation: Health Aff (Millwood). 1989 Summer;8(2):35-47
references.pmid: 2501203
references.citation: Health Policy. 2006 Dec;79(2-3):195-202
references.pmid: 16439035
references.citation: Health Econ. 2009 Apr;18(4):377-88
references.pmid: 18615835
references.citation: Health Policy. 2001 May;56(2):149-63
references.pmid: 11275303