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Relating illness complexity to reimbursement in CKD patients

Authors Bessette R, Carter

Published 19 September 2011 Volume 2011:4 Pages 121—130


Review by Single anonymous peer review

Peer reviewer comments 2

Russell W Bessette1, Randy L Carter2,3
1Department of Health Sciences, Institute for Healthcare Informatics, 2Department of Biostatistics, 3Population Health Observatory, University at Buffalo, State University of New York, Buffalo, NY, USA

Background: Despite significant investments of federal and state dollars to transition patient medical records to an all-electronic system, a chasm still exists between health care quality and payment for it. A major reason for this gap is the difficulty in evaluating health care outcomes based on claims data. Since both payers and patients may not appreciate how illness complexity impacts treatment outcomes, it is difficult to determine fair provider compensation.
Objectives: Chronic kidney disease (CKD) typifies these problems and is often associated with comorbidities that impact cost, health, and work productivity. Thus, the objective of this study was to evaluate an illness complexity score (ICS) based on a linear regression of select blood values that might assist in predicting average monthly reimbursements in CKD patients. A second objective was to compare the results of this ICS prediction to results obtained by prediction of average monthly reimbursement using CKD stage. A third objective was to analyze the relationship between the change in ICS, estimated glomerular filtration rate (eGFR), and CKD stage over time to average monthly reimbursement.
Methods: We calculated parsimonious values for select variables associated with CKD patients and compared the ICS to ordinal staging of renal disease. Data from 177 de-identified patients over 13 months was collected, which included 15 blood chemistry observations along with complete claims data for all medical expenses. To test for the relationship between average blood chemistry values, stages of CKD, age, and average monthly reimbursement, we modeled an association through a linear regression function of age, eGFR, and the Z-scores calculated from average monthly values of phosphorus, parathyroid hormone, glucose, hemoglobin, bicarbonate, albumin, creatinine, blood urea nitrogen, potassium, calcium, sodium, alkaline phosphatase, alanine aminotransferase, and white blood cells.
Results: The results of our study demonstrated that the association between average ICS values throughout the entire study period predicted average monthly reimbursements with an R2 value of 0.41. Comparing that value to the association between the average CKD stage and average monthly reimbursement demonstrated an R2 value of 0.08. Thus, ICS offers five times greater sensitivity over CKD staging as a measure of illness complexity.
Conclusion: Sorting the patient population by changes in CKD stage or ICS over the entire study period revealed significant differences between the two scoring methods. Groups scored by ICS demonstrated greater sensitivity by capturing dysfunction in other organ systems and had a better association with reimbursement than groups scored by CKD staging.

Keywords: chronic kidney disease, illness complexity score, reimbursement, disease severity

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